{
  "schema": "longtermcapabilities-machine-intelligence/v2",
  "version": "1.64.0",
  "releaseId": "lts-1.64.0-upgrade-safe-rewrites-and-privacy-erasure-integrity",
  "generated": "2026-08-03T20:40:00Z",
  "editorialDefinition": "Machine intelligence is the LongTermCapabilities editorial umbrella for systems that sense, interpret, predict, generate, plan, coordinate, or act with machine assistance. It is not asserted as a formal standard.",
  "decisionTools": {
    "architectureGuide": "/machine-intelligence/architecture-decision-guide/",
    "glossary": "/machine-intelligence/glossary/",
    "releaseGate": "/insights/ai-system-release-gate/",
    "failureTaxonomy": "/insights/ai-agent-failure-modes/"
  },
  "records": [
    {
      "path": "/machine-intelligence/",
      "title": "Machine intelligence is an operating system for consequential decisions",
      "summary": "A machine-intelligence system is more than a model. It is the complete arrangement of models, data, memory, tools, policies, people, evidence, and operational controls that turns uncertain computation into a governed decision or action.",
      "topic": "Machine intelligence",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Decide what kind of machine-intelligence architecture a consequential workflow actually needs—and which capabilities must remain human-owned.",
      "keywords": [
        "machine intelligence",
        "machine intelligence architecture",
        "AI governance",
        "AI evaluation",
        "agentic AI",
        "multi-agent systems",
        "swarm intelligence"
      ],
      "sources": [
        "https://www.nist.gov/itl/ai-risk-management-framework",
        "https://www.nist.gov/artificial-intelligence/ai-agent-standards-initiative",
        "https://www.nist.gov/programs-projects/building-evaluation-probes-agentic-ai",
        "https://www.nist.gov/news-events/news/2025/01/technical-blog-strengthening-ai-agent-hijacking-evaluations",
        "https://arxiv.org/abs/2503.13657",
        "https://arxiv.org/abs/2602.01011"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/agentic-ai/",
      "title": "Agentic AI needs a bounded control loop—not vague autonomy",
      "summary": "An agentic system observes a situation, selects a next step, uses bounded tools, evaluates the result, updates state, and either continues or stops. Production quality depends on every transition—not only on the model's final answer.",
      "topic": "Agentic AI",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Decide whether a workflow should use an agent, what authority it may hold, and what evidence is required before it can act.",
      "keywords": [
        "agentic AI",
        "AI agents",
        "agentic AI architecture",
        "AI agent security",
        "AI agent evaluation",
        "AI agent governance",
        "MCP",
        "A2A"
      ],
      "sources": [
        "https://www.nist.gov/artificial-intelligence/ai-agent-standards-initiative",
        "https://www.nist.gov/programs-projects/building-evaluation-probes-agentic-ai",
        "https://www.nist.gov/news-events/news/2025/01/technical-blog-strengthening-ai-agent-hijacking-evaluations",
        "https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/",
        "https://genai.owasp.org/resource/securing-agentic-applications-guide-1-0/",
        "https://modelcontextprotocol.io/specification/2026-07-28",
        "https://modelcontextprotocol.io/specification/2026-07-28/architecture",
        "https://modelcontextprotocol.io/specification/2026-07-28/server/tools",
        "https://a2a-protocol.org/latest/",
        "https://a2a-protocol.org/latest/topics/a2a-and-mcp/"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/multi-agent-systems/",
      "title": "Multi-agent systems trade one hard problem for a coordination problem",
      "summary": "Multiple agents can separate roles, parallelize work, and provide independent challenge. They can also multiply ambiguity, messages, cost, correlated errors, and recovery paths. The architecture must justify the team—not merely display one.",
      "topic": "Multi-agent systems",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Decide whether multiple agents create measurable value over one well-designed agent or deterministic workflow.",
      "keywords": [
        "multi-agent systems",
        "multi-agent AI",
        "agent teams",
        "AI orchestration",
        "agent coordination",
        "multi-agent evaluation"
      ],
      "sources": [
        "https://arxiv.org/abs/2308.08155",
        "https://arxiv.org/abs/2501.06322",
        "https://arxiv.org/abs/2510.10047",
        "https://arxiv.org/abs/2503.13657",
        "https://arxiv.org/abs/2602.01011",
        "https://arxiv.org/abs/2505.04364",
        "https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/",
        "https://genai.owasp.org/resource/securing-agentic-applications-guide-1-0/"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/swarm-intelligence/",
      "title": "Swarm intelligence is decentralized coordination—not a crowd of chatbots",
      "summary": "A swarm coordinates through local information, repeated interaction, feedback, and distributed decision rules. Global behavior emerges from the population. That can create resilience and scale—and make causality, authority, and safety harder to inspect.",
      "topic": "Swarm intelligence",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Decide whether decentralized local coordination is genuinely required, or whether a simpler supervised team will be safer and easier to operate.",
      "keywords": [
        "swarm intelligence",
        "swarm agents",
        "agent swarms",
        "decentralized AI",
        "stigmergy",
        "quorum sensing",
        "swarm coordination",
        "emergent behavior"
      ],
      "sources": [
        "https://arxiv.org/abs/2505.04364",
        "https://arxiv.org/abs/2503.13657",
        "https://arxiv.org/abs/2602.01011",
        "https://arxiv.org/abs/2501.06322",
        "https://arxiv.org/abs/2510.10047"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/agentic-ai-architecture-control-loop/",
      "title": "Design the agentic control loop before selecting the framework",
      "summary": "Framework selection is downstream of the operating model. The control loop determines state, authority, tool use, verification, failure recovery, and when the system must stop.",
      "topic": "Agentic AI architecture",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Decide which control-loop transitions are machine-owned, policy-owned, or human-owned.",
      "keywords": [
        "agentic AI architecture",
        "AI agent control loop",
        "AI agent planning",
        "AI agent tools",
        "human approval"
      ],
      "sources": [
        "https://www.nist.gov/artificial-intelligence/ai-agent-standards-initiative",
        "https://www.nist.gov/programs-projects/building-evaluation-probes-agentic-ai",
        "https://www.nist.gov/news-events/news/2025/01/technical-blog-strengthening-ai-agent-hijacking-evaluations",
        "https://genai.owasp.org/resource/securing-agentic-applications-guide-1-0/"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/agent-memory-state-provenance/",
      "title": "Agent memory is safe only when the system knows where it came from",
      "summary": "Remembering more is not the same as knowing better. Production memory needs source, authority, time, scope, trust state, retention, and supersession.",
      "topic": "Agent memory and state",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Decide which information an agent may retain, retrieve, trust, and use as instruction.",
      "keywords": [
        "AI agent memory",
        "agent state",
        "AI memory provenance",
        "vector memory security",
        "durable agent workflows"
      ],
      "sources": [
        "https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/",
        "https://genai.owasp.org/resource/securing-agentic-applications-guide-1-0/",
        "https://www.nist.gov/itl/ai-risk-management-framework"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/agentic-ai-evaluation/",
      "title": "Evaluate what the agent did—not only what it said",
      "summary": "A plausible final answer can hide unauthorized retrieval, unsafe tool use, repeated side effects, excessive cost, or a trajectory that cannot be reproduced. Agent evaluation must inspect the system path.",
      "topic": "Agentic AI evaluation",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Decide whether an agent has enough representative evidence and operational control to proceed, narrow, remediate, remain in pilot, or stop.",
      "keywords": [
        "agentic AI evaluation",
        "AI agent testing",
        "agent evaluation framework",
        "AI release gates",
        "agent traces"
      ],
      "sources": [
        "https://www.nist.gov/programs-projects/building-evaluation-probes-agentic-ai",
        "https://www.nist.gov/news-events/news/2025/01/technical-blog-strengthening-ai-agent-hijacking-evaluations",
        "https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/",
        "https://arxiv.org/abs/2503.13657",
        "https://arxiv.org/abs/2602.01011",
        "https://arxiv.org/abs/2505.04364"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/agentic-ai-security/",
      "title": "Agentic AI security begins with identity, authority, and effects",
      "summary": "An agent is not dangerous because it can generate text. Risk changes when generated choices are connected to identities, credentials, memory, tools, and real-world effects.",
      "topic": "Agentic AI security",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Decide which agent capabilities can be safely exposed under least authority, inspection, and independent containment.",
      "keywords": [
        "agentic AI security",
        "AI agent security",
        "prompt injection",
        "tool misuse",
        "AI identity",
        "agent containment"
      ],
      "sources": [
        "https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/",
        "https://genai.owasp.org/resource/securing-agentic-applications-guide-1-0/",
        "https://www.nist.gov/news-events/news/2025/01/technical-blog-strengthening-ai-agent-hijacking-evaluations",
        "https://modelcontextprotocol.io/specification/2026-07-28",
        "https://a2a-protocol.org/latest/",
        "https://a2a-protocol.org/latest/topics/a2a-and-mcp/"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/mcp-a2a-agent-interoperability/",
      "title": "Interoperability connects capabilities; governance decides whether they should be used",
      "summary": "MCP and A2A solve different connection problems. Neither protocol replaces identity, authorization, evidence, or a decision about which capabilities belong in the workflow.",
      "topic": "Agent interoperability",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Decide which interactions should be tool-oriented, which should be agent-to-agent, and what trust envelope applies to each.",
      "keywords": [
        "MCP",
        "Model Context Protocol",
        "A2A",
        "Agent2Agent Protocol",
        "agent interoperability",
        "AI tools"
      ],
      "sources": [
        "https://modelcontextprotocol.io/specification/2026-07-28",
        "https://modelcontextprotocol.io/specification/2026-07-28/architecture",
        "https://modelcontextprotocol.io/specification/2026-07-28/server/tools",
        "https://a2a-protocol.org/latest/",
        "https://a2a-protocol.org/latest/topics/a2a-and-mcp/"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/multi-agent-coordination-patterns/",
      "title": "Choose a coordination pattern that makes authority and failure visible",
      "summary": "The team topology determines where context, authority, cost, and failure concentrate. Select it from the task—not from the framework demo.",
      "topic": "Multi-agent coordination",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Decide which coordination topology matches the work and what simpler baseline it must outperform.",
      "keywords": [
        "multi-agent coordination",
        "agent orchestration patterns",
        "supervisor agent",
        "blackboard architecture",
        "agent auction"
      ],
      "sources": [
        "https://arxiv.org/abs/2308.08155",
        "https://arxiv.org/abs/2501.06322",
        "https://arxiv.org/abs/2510.10047",
        "https://arxiv.org/abs/2503.13657",
        "https://arxiv.org/abs/2602.01011"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/swarm-agent-techniques/",
      "title": "Swarm agent techniques coordinate through signals, thresholds, and local rules",
      "summary": "The useful part of a swarm is not the number of agents. It is the coordination mechanism that turns local observations into collective behavior.",
      "topic": "Swarm intelligence",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Decide which decentralized coordination technique fits the problem and how its emergent behavior will be measured and contained.",
      "keywords": [
        "swarm agent techniques",
        "stigmergy",
        "quorum sensing",
        "ant colony optimization",
        "particle swarm",
        "decentralized agents"
      ],
      "sources": [
        "https://arxiv.org/abs/2505.04364",
        "https://arxiv.org/abs/2501.06322",
        "https://arxiv.org/abs/2510.10047",
        "https://arxiv.org/abs/2503.13657"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/when-not-to-use-multi-agent-ai/",
      "title": "Use multiple agents only when the coordination cost buys something real",
      "summary": "A multi-agent diagram can make a simple workflow look advanced. It can also make responsibility, state, evaluation, and recovery materially harder.",
      "topic": "Multi-agent architecture",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Decide whether role separation or decentralization creates enough value to justify the added coordination and control surface.",
      "keywords": [
        "when not to use multi-agent AI",
        "single agent vs multi-agent",
        "agent architecture decision",
        "AI orchestration complexity"
      ],
      "sources": [
        "https://arxiv.org/abs/2503.13657",
        "https://arxiv.org/abs/2602.01011",
        "https://arxiv.org/abs/2501.06322",
        "https://arxiv.org/abs/2505.04364"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/observability-reliability-agentic-systems/",
      "title": "Observe the decision path, the side effects, and the recovery—not only the model",
      "summary": "An agent can return a good answer while the workflow leaks data, repeats an action, exceeds its budget, or leaves the system in an unknown state. Reliability must cover the complete run.",
      "topic": "Agentic AI reliability",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Decide which telemetry and recovery controls are required before an agentic workflow can support production operations.",
      "keywords": [
        "agentic AI observability",
        "AI agent reliability",
        "agent traces",
        "durable execution",
        "AI incident response"
      ],
      "sources": [
        "https://www.nist.gov/itl/ai-risk-management-framework",
        "https://www.nist.gov/programs-projects/building-evaluation-probes-agentic-ai",
        "https://genai.owasp.org/resource/securing-agentic-applications-guide-1-0/",
        "https://arxiv.org/abs/2503.13657"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/machine-intelligence/architecture-decision-guide/",
      "title": "Choose a workflow, RAG system, agent, multi-agent team, or swarm on evidence",
      "summary": "Agent count is not a maturity model. Start with the decision, the evidence, the authority, and the simplest architecture that can meet the operating need.",
      "topic": "Machine intelligence architecture",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Select a starting architecture and identify the evidence that must exist before increasing autonomy or coordination complexity.",
      "keywords": [
        "AI architecture decision guide",
        "RAG vs agent",
        "single agent vs multi-agent",
        "swarm AI decision",
        "AI systems architecture"
      ],
      "sources": [
        "https://www.nist.gov/itl/ai-risk-management-framework",
        "https://arxiv.org/abs/2503.13657",
        "https://arxiv.org/abs/2602.01011",
        "https://arxiv.org/abs/2601.07711",
        "https://arxiv.org/abs/2602.16666"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/machine-intelligence/glossary/",
      "title": "A practical glossary for AI systems, agents, coordination, authority, and evidence",
      "summary": "Precise language prevents architecture and authority decisions from being hidden behind labels such as agent, autonomy, memory, or human in the loop.",
      "topic": "Machine intelligence vocabulary",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Establish shared terms before evaluating or procuring an AI-enabled system.",
      "keywords": [
        "agentic AI glossary",
        "machine intelligence glossary",
        "AI agent terms",
        "multi-agent terminology",
        "swarm intelligence definitions"
      ],
      "sources": [
        "https://www.nist.gov/itl/ai-risk-management-framework",
        "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf",
        "https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/",
        "https://modelcontextprotocol.io/specification/2026-07-28",
        "https://a2a-protocol.org/latest/",
        "https://opentelemetry.io/blog/2026/genai-observability/",
        "https://arxiv.org/abs/2501.06322",
        "https://arxiv.org/abs/2505.04364"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/ai-agent-governance/",
      "title": "Govern agents through authority, ownership, evidence, and change control",
      "summary": "A policy that says agents must be safe is not an operating control. Governance becomes real when ownership, authority, evidence, change, monitoring, and stopping are built into the system.",
      "topic": "AI agent governance",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Define the operating model that determines who may create, approve, change, monitor, pause, and retire an agentic system.",
      "keywords": [
        "AI agent governance",
        "agentic AI governance",
        "AI agent controls",
        "AI governance operating model",
        "agent change control"
      ],
      "sources": [
        "https://www.nist.gov/itl/ai-risk-management-framework",
        "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.800-4.pdf",
        "https://modelcontextprotocol.io/docs/2026-07-28/tutorials/security/authorization",
        "https://blog.modelcontextprotocol.io/posts/enterprise-managed-auth/",
        "https://arxiv.org/abs/2501.09674",
        "https://genai.owasp.org/resource/securing-agentic-applications-guide-1-0/"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/human-authority-ai-systems/",
      "title": "Human in the loop is not a control unless the person has authority, evidence, time, and a usable interface",
      "summary": "Adding an approval button does not make an AI workflow accountable. The reviewer must understand the decision, see the evidence, have time to intervene, and hold a real right to refuse or escalate.",
      "topic": "Human authority",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Choose the human-AI configuration and decision rights required for each consequential transition in a workflow.",
      "keywords": [
        "human in the loop AI",
        "human oversight AI",
        "human authority agentic AI",
        "AI approval workflow",
        "human on the loop"
      ],
      "sources": [
        "https://www.nist.gov/itl/ai-risk-management-framework",
        "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf",
        "https://arxiv.org/abs/2507.22358",
        "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.800-4.pdf",
        "https://genai.owasp.org/resource/securing-agentic-applications-guide-1-0/"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/agentic-rag-architecture/",
      "title": "Use agentic retrieval only when adaptive evidence work justifies the added cost and control surface",
      "summary": "A fixed RAG pipeline retrieves evidence and generates an answer. Agentic RAG adds planning, iterative retrieval, source selection, verification, or delegation—and must earn that complexity.",
      "topic": "Agentic retrieval",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Decide whether a bounded retrieval pipeline is sufficient or whether adaptive retrieval materially improves the named workflow.",
      "keywords": [
        "agentic RAG architecture",
        "RAG vs agentic RAG",
        "adaptive retrieval",
        "RAG evaluation",
        "AI retrieval agent"
      ],
      "sources": [
        "https://arxiv.org/abs/2501.09136",
        "https://arxiv.org/abs/2601.07711",
        "https://arxiv.org/abs/2504.14891",
        "https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/",
        "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.800-4.pdf"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/ai-agent-failure-modes/",
      "title": "Turn agent failure modes into tests, telemetry, and recovery decisions",
      "summary": "A final answer can look acceptable while the trajectory violates authority, loses state, duplicates an effect, or leaves the system unrecoverable. Failure categories should map directly to tests and controls.",
      "topic": "Agentic AI evaluation",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Define which agentic failure classes matter for the workflow and how each will be detected, contained, and reviewed.",
      "keywords": [
        "AI agent failure modes",
        "agentic AI failure taxonomy",
        "AI agent testing",
        "agent trajectory failure",
        "multi-agent failures"
      ],
      "sources": [
        "https://arxiv.org/abs/2605.08545",
        "https://arxiv.org/abs/2605.20530",
        "https://arxiv.org/abs/2503.13657",
        "https://arxiv.org/abs/2511.04032",
        "https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/",
        "https://arxiv.org/abs/2602.16666"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/ai-system-release-gate/",
      "title": "Release, conditionally release, hold, or stop an AI system on explicit evidence",
      "summary": "A release gate is a documented decision—not a dashboard score. It connects evidence, unknowns, authority, residual risk, operating limits, and the next review date.",
      "topic": "AI production readiness",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Record whether a named AI-enabled workflow should be released, conditionally released, held, or stopped.",
      "keywords": [
        "AI release gate",
        "AI production readiness checklist",
        "agentic AI release criteria",
        "AI system approval",
        "AI evaluation evidence"
      ],
      "sources": [
        "https://www.nist.gov/itl/ai-risk-management-framework",
        "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf",
        "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.800-4.pdf",
        "https://genai.owasp.org/resource/securing-agentic-applications-guide-1-0/",
        "https://arxiv.org/abs/2602.16666"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/agent-identity-delegation/",
      "title": "Every agent action should answer who is acting, for whom, with which authority, and through which chain",
      "summary": "A shared API key can make an agent powerful, but it cannot establish which human authorized an action, which task it served, or whether a subordinate participant widened the scope.",
      "topic": "Agent identity and authorization",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Design an identity and delegation chain that preserves the human principal, narrows authority, and supports revocation and audit.",
      "keywords": [
        "AI agent identity",
        "agent delegation",
        "AI authorization",
        "MCP OAuth",
        "agent access control",
        "delegated authority AI"
      ],
      "sources": [
        "https://arxiv.org/abs/2501.09674",
        "https://modelcontextprotocol.io/docs/2026-07-28/tutorials/security/authorization",
        "https://blog.modelcontextprotocol.io/posts/enterprise-managed-auth/",
        "https://modelcontextprotocol.io/specification/2026-07-28",
        "https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/",
        "https://a2a-protocol.org/latest/"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/ai-evaluation-dataset-design/",
      "title": "Build evaluation cases that represent the decision, the edge conditions, and the ways the system can fail",
      "summary": "A benchmark becomes decision evidence only when its cases represent the real workflow, consequences, data boundaries, and change conditions of the system being released.",
      "topic": "AI evaluation",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Define a versioned evaluation corpus and adjudication process that can support release and regression decisions.",
      "keywords": [
        "AI evaluation dataset",
        "agent evaluation cases",
        "RAG test dataset",
        "AI red team dataset",
        "agentic AI benchmark design"
      ],
      "sources": [
        "https://arxiv.org/abs/2605.08545",
        "https://arxiv.org/abs/2605.20530",
        "https://arxiv.org/abs/2504.14891",
        "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.800-4.pdf",
        "https://www.nist.gov/programs-projects/building-evaluation-probes-agentic-ai",
        "https://arxiv.org/abs/2602.16666"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/enterprise-ai-buyer-evidence/",
      "title": "Give enterprise buyers a reviewable AI evidence package—not a stack of unsupported claims",
      "summary": "A serious buyer needs to understand what the AI-enabled system does, where data moves, who retains authority, what was tested, and what remains unknown—without receiving sensitive internal detail by default.",
      "topic": "AI buyer readiness",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Define the public, qualified-access, and private evidence needed for an enterprise or government-adjacent AI review.",
      "keywords": [
        "enterprise AI buyer evidence",
        "AI procurement checklist",
        "AI trust center",
        "AI security questionnaire",
        "AI system documentation"
      ],
      "sources": [
        "https://www.nist.gov/itl/ai-risk-management-framework",
        "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf",
        "https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/",
        "https://modelcontextprotocol.io/docs/2026-07-28/tutorials/security/authorization",
        "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.800-4.pdf"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/machine-intelligence/reference-architecture/",
      "title": "A machine-intelligence reference architecture separates decision, authority, evidence, and execution",
      "summary": "The model is one component. The operating capability is the complete path from a named decision through authorized evidence, bounded computation, verified effects, human authority, and recoverable operations.",
      "topic": "Machine intelligence architecture",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Define the planes and boundaries that must exist before a machine-intelligence workflow can be evaluated or released.",
      "keywords": [
        "machine intelligence reference architecture",
        "AI system architecture",
        "AI control plane",
        "AI governance architecture",
        "agentic AI architecture diagram"
      ],
      "sources": [
        "https://www.nist.gov/itl/ai-risk-management-framework",
        "https://www.nist.gov/artificial-intelligence/ai-agent-standards-initiative",
        "https://csrc.nist.gov/pubs/other/2026/02/05/accelerating-the-adoption-of-software-and-ai-agent/ipd",
        "https://modelcontextprotocol.io/specification/2026-07-28",
        "https://modelcontextprotocol.io/specification/2026-07-28/basic/authorization",
        "https://modelcontextprotocol.io/docs/draft/tutorials/security/security_best_practices",
        "https://opentelemetry.io/docs/specs/semconv/",
        "https://github.com/open-telemetry/semantic-conventions-genai/blob/main/docs/gen-ai/gen-ai-agent-spans.md"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/agentic-ai/reference-architecture/",
      "title": "An agentic AI architecture needs a control loop, policy gateway, effect verification, and independent stop path",
      "summary": "A production agent is a stateful control loop around uncertain computation. The architecture must bound what the loop can observe, decide, request, execute, remember, delegate, retry, and stop.",
      "topic": "Agentic AI architecture",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Define the control and execution boundaries for one agentic workflow before selecting a framework or adding more agents.",
      "keywords": [
        "agentic AI reference architecture",
        "AI agent control plane",
        "AI agent tool gateway",
        "AI agent architecture diagram",
        "agentic workflow architecture"
      ],
      "sources": [
        "https://www.nist.gov/artificial-intelligence/ai-agent-standards-initiative",
        "https://csrc.nist.gov/pubs/other/2026/02/05/accelerating-the-adoption-of-software-and-ai-agent/ipd",
        "https://www.nist.gov/publications/summary-analysis-responses-request-information-regarding-security-considerations-ai",
        "https://modelcontextprotocol.io/specification/2026-07-28",
        "https://modelcontextprotocol.io/specification/2026-07-28/basic/authorization",
        "https://modelcontextprotocol.io/docs/draft/tutorials/security/security_best_practices",
        "https://a2a-protocol.org/latest/specification/",
        "https://a2a-protocol.org/latest/topics/streaming-and-async/",
        "https://github.com/open-telemetry/semantic-conventions-genai/blob/main/docs/gen-ai/gen-ai-agent-spans.md",
        "https://genai.owasp.org/resource/securing-agentic-applications-guide-1-0/"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/multi-agent-systems/topology-decision-matrix/",
      "title": "Choose a multi-agent topology by coordination need, not by the number of agent roles",
      "summary": "A topology is a coordination contract. It determines who can assign work, who sees shared state, how disagreement is resolved, where authority lives, and how a partially completed team task is recovered.",
      "topic": "Multi-agent systems",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Select a topology only when it demonstrates value over a strong single-agent baseline and has a testable recovery model.",
      "keywords": [
        "multi-agent topology",
        "multi-agent architecture patterns",
        "supervisor agent",
        "blackboard multi-agent system",
        "federated agents",
        "swarm topology"
      ],
      "sources": [
        "https://a2a-protocol.org/latest/specification/",
        "https://a2a-protocol.org/latest/topics/streaming-and-async/",
        "https://arxiv.org/abs/2503.13657",
        "https://arxiv.org/abs/2602.01011",
        "https://arxiv.org/abs/2510.10047",
        "https://arxiv.org/abs/2505.04364",
        "https://github.com/open-telemetry/semantic-conventions-genai/blob/main/docs/gen-ai/gen-ai-agent-spans.md"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/swarm-intelligence/engineering-patterns/",
      "title": "Swarm intelligence needs local rules, negative feedback, global invariants, and independent containment",
      "summary": "A swarm is not a group chat among agents. It is a decentralized control system in which local rules and environmental feedback produce system-level behavior.",
      "topic": "Swarm intelligence",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Use swarm-style coordination only when decentralization is intrinsic and the organization can test emergence, invariants, and containment.",
      "keywords": [
        "swarm intelligence patterns",
        "swarm agent techniques",
        "stigmergy AI",
        "quorum sensing agents",
        "decentralized AI coordination",
        "swarm containment"
      ],
      "sources": [
        "https://arxiv.org/abs/2505.04364",
        "https://arxiv.org/abs/2503.13657",
        "https://arxiv.org/abs/2602.01011",
        "https://www.nist.gov/artificial-intelligence/ai-agent-standards-initiative",
        "https://www.nist.gov/publications/summary-analysis-responses-request-information-regarding-security-considerations-ai",
        "https://genai.owasp.org/resource/securing-agentic-applications-guide-1-0/"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/agent-tool-contracts/",
      "title": "An AI agent tool needs a contract for authority, effects, verification, retries, and evidence",
      "summary": "A tool definition that contains only a name, description, and JSON arguments is not an operating contract for a consequential action.",
      "topic": "Agent tools",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Define what each tool can do, under whose authority, with which limits, and how its external effect is verified before an agent can use it.",
      "keywords": [
        "AI agent tool contract",
        "agent tool authorization",
        "MCP tool security",
        "AI tool idempotency",
        "agent tool schema",
        "agentic AI tool gateway"
      ],
      "sources": [
        "https://modelcontextprotocol.io/specification/2026-07-28/server/tools",
        "https://modelcontextprotocol.io/specification/2026-07-28/basic/authorization",
        "https://modelcontextprotocol.io/docs/draft/tutorials/security/security_best_practices",
        "https://csrc.nist.gov/pubs/other/2026/02/05/accelerating-the-adoption-of-software-and-ai-agent/ipd",
        "https://www.nist.gov/publications/summary-analysis-responses-request-information-regarding-security-considerations-ai",
        "https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/",
        "https://genai.owasp.org/resource/securing-agentic-applications-guide-1-0/"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/agent-observability-trace-schema/",
      "title": "Agent observability must connect the task, identity, policy, tool effect, state transition, and human decision",
      "summary": "A final answer is not enough evidence for a system that can plan, call tools, change state, delegate work, and wait for human approval.",
      "topic": "Agent observability",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Define the minimum trace evidence needed to explain, evaluate, secure, operate, and recover one agentic workflow.",
      "keywords": [
        "AI agent observability",
        "agent trace schema",
        "OpenTelemetry agent spans",
        "agentic AI tracing",
        "AI tool call trace",
        "agent workflow telemetry"
      ],
      "sources": [
        "https://opentelemetry.io/docs/specs/semconv/",
        "https://github.com/open-telemetry/semantic-conventions-genai",
        "https://github.com/open-telemetry/semantic-conventions-genai/blob/main/docs/gen-ai/gen-ai-agent-spans.md",
        "https://opentelemetry.io/blog/2026/genai-observability/",
        "https://a2a-protocol.org/latest/specification/",
        "https://a2a-protocol.org/latest/topics/streaming-and-async/",
        "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.800-4.pdf",
        "https://arxiv.org/abs/2605.08545"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/agentic-ai-incident-response/",
      "title": "Agentic AI incident response must stop authority, preserve evidence, and reconcile external effects",
      "summary": "An agent incident can leave messages sent, records changed, code executed, work queued, credentials active, and business state uncertain even after the model endpoint is disabled.",
      "topic": "Agentic AI reliability",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Define how the organization detects, contains, reconstructs, reconciles, recovers, and validates one agentic failure family.",
      "keywords": [
        "agentic AI incident response",
        "AI agent incident runbook",
        "AI agent kill switch",
        "agentic AI recovery",
        "AI tool effect reconciliation"
      ],
      "sources": [
        "https://www.nist.gov/publications/summary-analysis-responses-request-information-regarding-security-considerations-ai",
        "https://atlas.mitre.org/",
        "https://csrc.nist.gov/pubs/ai/100/2/e2025/final",
        "https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/",
        "https://genai.owasp.org/resource/securing-agentic-applications-guide-1-0/",
        "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.800-4.pdf",
        "https://modelcontextprotocol.io/docs/draft/tutorials/security/security_best_practices",
        "https://a2a-protocol.org/latest/specification/"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/agentic-system-change-control/",
      "title": "Agentic system change control must track behavior, authority, data, tools, topology, and recovery",
      "summary": "A small implementation change can materially alter an agent's behavior, authority, cost, data exposure, or recovery path without changing the public feature name.",
      "topic": "AI change control",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Define which changes require routine regression, conditional release, full reassessment, or immediate hold.",
      "keywords": [
        "agentic AI change control",
        "AI model change management",
        "AI agent release management",
        "prompt change control",
        "AI tool change review"
      ],
      "sources": [
        "https://www.nist.gov/itl/ai-risk-management-framework",
        "https://csrc.nist.gov/pubs/other/2026/02/05/accelerating-the-adoption-of-software-and-ai-agent/ipd",
        "https://www.nist.gov/publications/summary-analysis-responses-request-information-regarding-security-considerations-ai",
        "https://modelcontextprotocol.io/specification/2026-07-28",
        "https://modelcontextprotocol.io/specification/2026-07-28/basic/authorization",
        "https://modelcontextprotocol.io/docs/draft/tutorials/security/security_best_practices",
        "https://a2a-protocol.org/latest/specification/",
        "https://github.com/open-telemetry/semantic-conventions-genai/blob/main/docs/gen-ai/gen-ai-agent-spans.md",
        "https://genai.owasp.org/resource/securing-agentic-applications-guide-1-0/"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/resources/agentic-system-evidence-workbook/",
      "title": "Use a compact evidence workbook to define an agentic system before release",
      "summary": "The workbook turns an agent concept into a reviewable set of boundaries, contracts, traces, decisions, and recovery evidence without claiming certification or production approval.",
      "topic": "Agentic system evidence",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Assemble the minimum architecture and evidence package needed to decide whether one agentic workflow is ready for deeper evaluation or release review.",
      "keywords": [
        "agentic AI workbook",
        "AI agent architecture template",
        "AI agent tool contract template",
        "AI agent trace schema",
        "AI incident response checklist"
      ],
      "sources": [
        "https://www.nist.gov/itl/ai-risk-management-framework",
        "https://www.nist.gov/artificial-intelligence/ai-agent-standards-initiative",
        "https://csrc.nist.gov/pubs/other/2026/02/05/accelerating-the-adoption-of-software-and-ai-agent/ipd",
        "https://www.nist.gov/publications/summary-analysis-responses-request-information-regarding-security-considerations-ai",
        "https://modelcontextprotocol.io/specification/2026-07-28",
        "https://a2a-protocol.org/latest/specification/",
        "https://opentelemetry.io/docs/specs/semconv/",
        "https://genai.owasp.org/resource/securing-agentic-applications-guide-1-0/"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/agentic-ai/security-assurance/",
      "title": "Secure the authority path, not only the model",
      "summary": "A model can resist one prompt attack while the surrounding system still delegates the wrong authority, trusts a malicious peer, repeats an irreversible action, or cannot be stopped independently.",
      "topic": "Agentic AI security",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Decide whether the complete agentic authority path has enough evidence for a production release or requires containment, redesign, or further evaluation.",
      "keywords": [
        "agentic AI security assurance",
        "AI agent security architecture",
        "AI agent authorization",
        "agent identity",
        "agent containment",
        "AI red teaming"
      ],
      "sources": [
        "https://www.nist.gov/artificial-intelligence/ai-agent-standards-initiative",
        "https://www.nist.gov/publications/summary-analysis-responses-request-information-regarding-security-considerations-ai",
        "https://www.nist.gov/news-events/news/2025/01/technical-blog-strengthening-ai-agent-hijacking-evaluations",
        "https://www.nist.gov/blogs/caisi-research-blog/insights-ai-agent-security-large-scale-red-teaming-competition",
        "https://csrc.nist.gov/pubs/other/2026/02/05/accelerating-the-adoption-of-software-and-ai-agent/ipd",
        "https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/",
        "https://genai.owasp.org/resource/securing-agentic-applications-guide-1-0/",
        "https://genai.owasp.org/resource/multi-agentic-system-threat-modeling-guide-v1-0/",
        "https://modelcontextprotocol.io/specification/2026-07-28/basic/authorization",
        "https://modelcontextprotocol.io/docs/draft/tutorials/security/security_best_practices",
        "https://a2a-protocol.org/latest/topics/enterprise-ready/"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/machine-intelligence/evaluation-plan/",
      "title": "Evaluate the outcome, the trajectory, the authority, and the recovery",
      "summary": "A useful final answer can conceal an unsafe tool call, an unauthorized data path, a poisoned memory, a coordination failure, or a recovery process that no operator can execute.",
      "topic": "Machine intelligence evaluation",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Define the evidence required to release, conditionally release, hold, or stop one named machine-intelligence workflow.",
      "keywords": [
        "AI agent evaluation plan",
        "agentic AI evaluation",
        "machine intelligence evaluation",
        "AI release evaluation",
        "multi-agent evaluation",
        "AI system testing"
      ],
      "sources": [
        "https://www.nist.gov/itl/ai-risk-management-framework",
        "https://www.nist.gov/programs-projects/building-evaluation-probes-agentic-ai",
        "https://www.nist.gov/news-events/news/2025/01/technical-blog-strengthening-ai-agent-hijacking-evaluations",
        "https://www.nist.gov/blogs/caisi-research-blog/insights-ai-agent-security-large-scale-red-teaming-competition",
        "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.800-4.pdf",
        "https://csrc.nist.gov/pubs/ai/100/2/e2025/final",
        "https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/",
        "https://arxiv.org/abs/2503.13657",
        "https://arxiv.org/abs/2602.01011"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/multi-agent-systems/threat-model/",
      "title": "Threat-model the relationships between agents, not only each agent",
      "summary": "A collection of individually secured agents can still fail as a system when trust, authority, messages, state, or recovery cross agent boundaries.",
      "topic": "Multi-agent security",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Decide whether each inter-agent trust boundary has an explicit identity, authorization, evidence, failure, and containment model.",
      "keywords": [
        "multi-agent threat model",
        "agent-to-agent security",
        "A2A security",
        "multi-agent system security",
        "agent delegation",
        "shared memory security"
      ],
      "sources": [
        "https://genai.owasp.org/resource/multi-agentic-system-threat-modeling-guide-v1-0/",
        "https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/",
        "https://a2a-protocol.org/latest/specification/",
        "https://a2a-protocol.org/latest/topics/enterprise-ready/",
        "https://a2a-protocol.org/latest/topics/streaming-and-async/",
        "https://modelcontextprotocol.io/docs/draft/tutorials/security/security_best_practices",
        "https://modelcontextprotocol.io/specification/2026-07-28/basic/authorization",
        "https://csrc.nist.gov/pubs/other/2026/02/05/accelerating-the-adoption-of-software-and-ai-agent/ipd",
        "https://www.nist.gov/publications/summary-analysis-responses-request-information-regarding-security-considerations-ai"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/swarm-intelligence/simulation-and-safety/",
      "title": "Test emergence before decentralized behavior reaches production",
      "summary": "Local rules can look harmless in isolation and still produce herding, oscillation, starvation, runaway resource use, or an unsafe global state.",
      "topic": "Swarm intelligence safety",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Decide whether decentralized coordination is stable, bounded, explainable enough, and measurably better than a simpler alternative.",
      "keywords": [
        "swarm intelligence simulation",
        "swarm agent safety",
        "decentralized AI agents",
        "emergent behavior testing",
        "multi-agent simulation",
        "swarm containment"
      ],
      "sources": [
        "https://arxiv.org/abs/2505.04364",
        "https://arxiv.org/abs/2503.13657",
        "https://arxiv.org/abs/2602.01011",
        "https://arxiv.org/abs/2501.06322",
        "https://genai.owasp.org/resource/multi-agentic-system-threat-modeling-guide-v1-0/",
        "https://www.nist.gov/publications/summary-analysis-responses-request-information-regarding-security-considerations-ai",
        "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.800-4.pdf"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/agent-hijacking-evaluation/",
      "title": "Test whether untrusted content can redirect the agent's objective or authority path",
      "summary": "The decisive question is not whether the model recognizes a malicious string. It is whether untrusted content can alter the effective objective, tool path, state, or external effect.",
      "topic": "Agentic AI security evaluation",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Define an adaptive hijacking test program and the failures that block production release.",
      "keywords": [
        "agent hijacking evaluation",
        "AI agent prompt injection testing",
        "indirect prompt injection",
        "agentic AI red teaming",
        "AI agent security evaluation"
      ],
      "sources": [
        "https://www.nist.gov/news-events/news/2025/01/technical-blog-strengthening-ai-agent-hijacking-evaluations",
        "https://www.nist.gov/blogs/caisi-research-blog/insights-ai-agent-security-large-scale-red-teaming-competition",
        "https://www.nist.gov/publications/summary-analysis-responses-request-information-regarding-security-considerations-ai",
        "https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/",
        "https://genai.owasp.org/resource/securing-agentic-applications-guide-1-0/",
        "https://csrc.nist.gov/pubs/ai/100/2/e2025/final",
        "https://modelcontextprotocol.io/docs/draft/tutorials/security/security_best_practices"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/mcp-authorization-and-security/",
      "title": "Treat MCP as a protocol boundary, not a security conclusion",
      "summary": "Connecting a model to an MCP server expands capability. It does not by itself establish who may call which tool, which data may leave, or whether an external effect is safe and verified.",
      "topic": "MCP security",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Decide whether an MCP integration has an explicit identity, authorization, consent, policy, tool-contract, effect-verification, and audit model.",
      "keywords": [
        "MCP authorization",
        "Model Context Protocol security",
        "MCP OAuth 2.1",
        "MCP token passthrough",
        "MCP tool security",
        "MCP 2026-07-28"
      ],
      "sources": [
        "https://modelcontextprotocol.io/specification/2026-07-28",
        "https://modelcontextprotocol.io/specification/2026-07-28",
        "https://modelcontextprotocol.io/specification/2026-07-28/basic/authorization",
        "https://modelcontextprotocol.io/docs/draft/tutorials/security/security_best_practices",
        "https://modelcontextprotocol.io/specification/2026-07-28/server/tools",
        "https://csrc.nist.gov/pubs/other/2026/02/05/accelerating-the-adoption-of-software-and-ai-agent/ipd",
        "https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/insights/ai-agent-cost-and-budget-controls/",
      "title": "Measure cost per verified outcome, not cost per response",
      "summary": "An inexpensive model response can become an expensive workflow after retrieval, tools, retries, peer messages, human review, reconciliation, and recovery are included.",
      "topic": "Agentic AI economics",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Define hard resource envelopes and decide whether the system produces sufficient verified value within them.",
      "keywords": [
        "AI agent cost control",
        "agentic AI budget",
        "AI token budget",
        "multi-agent cost",
        "AI agent FinOps",
        "cost per verified outcome"
      ],
      "sources": [
        "https://arxiv.org/abs/2503.13657",
        "https://arxiv.org/abs/2602.01011",
        "https://arxiv.org/abs/2505.04364",
        "https://www.nist.gov/itl/ai-risk-management-framework",
        "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.800-4.pdf",
        "https://opentelemetry.io/blog/2026/genai-observability/"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/resources/agentic-security-evaluation-checklist/",
      "title": "A public-safe checklist for the evidence needed before consequential agentic use",
      "summary": "Use the checklist to expose missing evidence and assign owners. Do not turn it into a universal score or a substitute for system-specific testing.",
      "topic": "Agentic AI evidence resource",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Identify which security, evaluation, authority, recovery, and economics records are missing before a production decision.",
      "keywords": [
        "agentic AI security checklist",
        "AI agent evaluation checklist",
        "agent release checklist",
        "multi-agent threat model checklist",
        "MCP security checklist"
      ],
      "sources": [
        "https://www.nist.gov/artificial-intelligence/ai-agent-standards-initiative",
        "https://www.nist.gov/publications/summary-analysis-responses-request-information-regarding-security-considerations-ai",
        "https://www.nist.gov/blogs/caisi-research-blog/insights-ai-agent-security-large-scale-red-teaming-competition",
        "https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/",
        "https://genai.owasp.org/resource/multi-agentic-system-threat-modeling-guide-v1-0/",
        "https://modelcontextprotocol.io/specification/2026-07-28/basic/authorization",
        "https://modelcontextprotocol.io/docs/draft/tutorials/security/security_best_practices",
        "https://a2a-protocol.org/latest/topics/enterprise-ready/",
        "https://www.nist.gov/itl/ai-risk-management-framework"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/solution-guides/agentic-ai-production-readiness/",
      "title": "How to put an AI agent into production without losing control of authority, effects, or recovery",
      "summary": "A production agent is not simply a model with tools. It is an operating system of identity, delegated authority, context, external effects, durable state, evidence, human decisions, and recovery paths.",
      "topic": "Agentic AI production readiness",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Decide whether one named agentic workflow should remain research, proceed to bounded evaluation, release with conditions, hold, stop, or return to a simpler architecture.",
      "keywords": [
        "agentic AI production readiness",
        "AI agent production checklist",
        "deploy AI agents safely",
        "AI agent release readiness",
        "production AI agent architecture"
      ],
      "sources": [
        "https://www.nist.gov/itl/ai-risk-management-framework",
        "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf",
        "https://www.nist.gov/artificial-intelligence/ai-agent-standards-initiative",
        "https://www.nist.gov/publications/summary-analysis-responses-request-information-regarding-security-considerations-ai",
        "https://www.nist.gov/blogs/caisi-research-blog/insights-ai-agent-security-large-scale-red-teaming-competition",
        "https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/",
        "https://genai.owasp.org/resource/securing-agentic-applications-guide-1-0/",
        "https://modelcontextprotocol.io/specification/2026-07-28",
        "https://modelcontextprotocol.io/specification/2026-07-28/basic/authorization",
        "https://modelcontextprotocol.io/docs/draft/tutorials/security/security_best_practices",
        "https://a2a-protocol.org/latest/specification/",
        "https://a2a-protocol.org/latest/topics/enterprise-ready/",
        "https://arxiv.org/abs/2503.13657",
        "https://arxiv.org/abs/2602.01011"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/resources/ai-agent-release-decision-brief/",
      "title": "Build a local, exportable release-decision brief for one AI agent workflow",
      "summary": "Complete the fields in this browser, generate Markdown or JSON, and keep the result in your own records. The page does not send, store, or score the brief.",
      "topic": "AI agent decision resource",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Create a concise record of the workflow, architecture, consequence, human authority, evidence, gaps, current decision state, next action, and material-change triggers.",
      "keywords": [
        "AI agent release decision template",
        "agentic AI production readiness template",
        "AI agent decision brief",
        "AI release approval record"
      ],
      "sources": [
        "https://www.nist.gov/itl/ai-risk-management-framework",
        "https://www.nist.gov/artificial-intelligence/ai-agent-standards-initiative",
        "https://www.nist.gov/publications/summary-analysis-responses-request-information-regarding-security-considerations-ai",
        "https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/",
        "https://modelcontextprotocol.io/specification/2026-07-28"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/about/mike-kappel/",
      "title": "Mike Kappel — principal software architect",
      "summary": "Mike Kappel is the principal software architect behind LongTermCapabilities. He focuses on bounded decisions for critical Microsoft systems, governed machine intelligence, integration, reliability, and client-owned technical evidence.",
      "topic": "Principal profile",
      "articleType": "ProfilePage",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Understand who is accountable for the site's technical guidance and how the principal-led delivery model works.",
      "keywords": [
        "Mike Kappel",
        "principal software architect",
        "LongTermCapabilities author",
        "AI systems architect",
        "Microsoft modernization architect"
      ],
      "sources": [
        "https://developers.google.com/search/docs/fundamentals/creating-helpful-content",
        "https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/editorial-standards/",
      "title": "Editorial standards for technical authority and public evidence",
      "summary": "Authority comes from traceable evidence, useful technical judgment, explicit uncertainty, named human responsibility, and corrections—not from publishing the largest number of pages.",
      "topic": "Editorial standards",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Understand what supports a LongTermCapabilities public claim and how readers can interpret its evidence, dates, authorship, and limitations.",
      "keywords": [
        "technical editorial standards",
        "AI content source policy",
        "engineering content review",
        "machine intelligence research standards"
      ],
      "sources": [
        "https://developers.google.com/search/docs/fundamentals/ai-optimization-guide",
        "https://developers.google.com/search/docs/fundamentals/creating-helpful-content",
        "https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data",
        "https://blogs.bing.com/webmaster/February-2026/Introducing-AI-Performance-in-Bing-Webmaster-Tools-Public-Preview",
        "https://help.openai.com/en/articles/12627856-publishers-and-developers-faq"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/public-data/",
      "title": "Public machine-readable data and evidence resources",
      "summary": "The site publishes public-safe JSON, text, PDF, and structured metadata so people and machines can inspect service definitions, technical resources, evidence templates, routes, and release identity without scraping the visual interface.",
      "topic": "Public data catalog",
      "articleType": "Dataset",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Find the authoritative public data surface for a service, technical template, route, trust record, or release fact—and understand what is deliberately excluded.",
      "keywords": [
        "LongTermCapabilities public data",
        "machine-readable AI evidence",
        "AI schema templates",
        "JSON data catalog",
        "llms.txt",
        "public route index"
      ],
      "sources": [
        "https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data",
        "https://help.openai.com/en/articles/12627856-publishers-and-developers-faq"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    },
    {
      "path": "/machine-intelligence/answers/",
      "title": "Direct answers to machine intelligence and agentic AI questions",
      "summary": "Use these concise answers to establish the system boundary, identify the decisive evidence, and move to the deeper architecture, security, evaluation, and production-readiness guide for the question in front of you.",
      "topic": "Machine intelligence answers",
      "articleType": "TechArticle",
      "author": "Mike Kappel",
      "publishedDate": "2026-08-01",
      "lastReviewed": "2026-08-01",
      "decisionRelevance": "Find the simplest technically defensible answer and the next evidence question without treating a search snippet as production approval.",
      "keywords": [
        "machine intelligence questions",
        "agentic AI answers",
        "AI agent production",
        "multi-agent systems",
        "swarm intelligence",
        "MCP security",
        "AI evaluation"
      ],
      "sources": [
        "https://developers.google.com/search/docs/fundamentals/ai-optimization-guide",
        "https://blogs.bing.com/webmaster/February-2026/Introducing-AI-Performance-in-Bing-Webmaster-Tools-Public-Preview",
        "https://help.openai.com/en/articles/12627856-publishers-and-developers-faq",
        "https://www.nist.gov/itl/ai-risk-management-framework",
        "https://www.nist.gov/artificial-intelligence/ai-agent-standards-initiative",
        "https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/",
        "https://modelcontextprotocol.io/specification/2026-07-28",
        "https://a2a-protocol.org/latest/",
        "https://arxiv.org/abs/2503.13657",
        "https://arxiv.org/abs/2602.01011"
      ],
      "boundary": "Editorial research; not certification, legal advice, or an automated architecture or release approval."
    }
  ],
  "referenceArchitectures": {
    "machineIntelligence": "/machine-intelligence/reference-architecture/",
    "agenticAI": "/agentic-ai/reference-architecture/",
    "multiAgentTopologies": "/multi-agent-systems/topology-decision-matrix/",
    "swarmPatterns": "/swarm-intelligence/engineering-patterns/"
  },
  "operationalEvidence": {
    "toolContracts": "/insights/agent-tool-contracts/",
    "traceSchema": "/insights/agent-observability-trace-schema/",
    "incidentResponse": "/insights/agentic-ai-incident-response/",
    "changeControl": "/insights/agentic-system-change-control/",
    "workbook": "/resources/agentic-system-evidence-workbook/"
  },
  "reviewDate": "2026-08-01",
  "securityAndEvaluation": {
    "agenticSecurityAssurance": "/agentic-ai/security-assurance/",
    "evaluationPlan": "/machine-intelligence/evaluation-plan/",
    "multiAgentThreatModel": "/multi-agent-systems/threat-model/",
    "swarmSimulationSafety": "/swarm-intelligence/simulation-and-safety/",
    "agentHijackingEvaluation": "/insights/agent-hijacking-evaluation/",
    "mcpAuthorizationSecurity": "/insights/mcp-authorization-and-security/",
    "agentBudgetControls": "/insights/ai-agent-cost-and-budget-controls/",
    "checklist": "/resources/agentic-security-evaluation-checklist/"
  },
  "machineReadable": {
    "securityReview": "/data/agentic-security-review-checklist.json",
    "evaluationPlan": "/data/ai-agent-evaluation-plan.json",
    "multiAgentThreatModel": "/data/multi-agent-threat-model.json",
    "swarmSafety": "/data/swarm-simulation-safety-checklist.json",
    "mcpAuthorization": "/data/mcp-authorization-review.json",
    "budgetControls": "/data/agent-budget-control-register.json",
    "answerCards": "/data/machine-intelligence-answer-cards.json"
  },
  "productionReadiness": {
    "agenticGuide": "/solution-guides/agentic-ai-production-readiness/",
    "releaseDecisionBrief": "/resources/ai-agent-release-decision-brief/",
    "readinessMatrix": "/data/agentic-production-readiness-matrix.json",
    "briefSchema": "/data/ai-agent-release-decision-brief-schema.json"
  },
  "answerAuthority": {
    "answerHub": "/machine-intelligence/answers/",
    "answerCards": "/data/machine-intelligence-answer-cards.json",
    "principalProfile": "/about/mike-kappel/",
    "editorialStandards": "/editorial-standards/",
    "publicDataCatalog": "/public-data/"
  }
}