AgentOps: diagnose MCP tool shadowing before a production action
A production runbook for detecting MCP tool collisions, proving which tool the agent actually selected and validating a canary catalog before any sensitive action.
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32 articles connected to this technical signal.
A production runbook for detecting MCP tool collisions, proving which tool the agent actually selected and validating a canary catalog before any sensitive action.
Read articleA production runbook for proving that an agent fallback model preserves structured outputs, tool boundaries, refusals and traceability before routing live traffic.
Read articleA production runbook for isolating hostile instructions carried by MCP tool output, preserving provenance, enforcing policy outside the model, and validating or rolling back write access.
Read articleA production runbook for separating agent regression from evaluator drift by freezing outputs, replaying an adjudicated anchor set, measuring disagreement and keeping promotion rollbackable.
Read articleA production runbook for separating blocked prompts, filtered outputs, application errors and policy drift before changing Microsoft Foundry guardrails.
Read articleA production runbook for bounding evidence age across retrieval, telemetry and tool results before an AI agent proposes, executes or rolls back an operational action.
Read articleA production runbook for qualifying an AI agent regional failover across model deployment, state, retrieval, tools, identity, traces, canary traffic, validation and rollback.
Read articleA production runbook for detecting and revalidating stale human approval across state drift, request fingerprints, expiry, traces, refusal tests and rollback before an AI agent resumes an action.
Read articleA production runbook for detecting context leaks across AI agent sessions, separating memory, caches, retrieval, tools and identity, then enabling or rolling back persistence without losing audit traces.
Read articleA production runbook for reconstructing an AI agent's context, isolating history, retrieval and tool outputs, then validating compaction or rolling back.
Read articleA production runbook for proving that an AgentOps score is not inflated by leakage across evaluations, prompts, retrieval or tuning data before promoting an AI agent.
Read articleA production runbook to test a Toolbox version, its MCP tools, identities, approvals and traces before promotion, then return to the previous version without redeploying agents.
Read articleA production runbook for comparing a candidate agent with the active runtime on the same requests without duplicating actions, then deciding promotion, canary or rollback.
Read articleA production runbook for defining latency budgets, idempotency, retries, circuit breakers, traces and rollback for an MCP tool called by an AI agent.
Read articleA production runbook for qualifying structured AI agent tool output with schema, sources, diff, idempotence, policy, traces, human validation and rollback before writing to production.
Read articleA production runbook for qualifying MCP server drift with tool manifests, schemas, identity, secrets, network path, traces, evaluations, validation and rollback before reauthorizing an AI agent.
Read articleA production runbook for qualifying an internal MCP server with tool inventory, scopes, identities, secrets, audit, dry runs, evaluations, human validation and rollback before agent access.
Read articleA production runbook for qualifying a failed AI agent tool call with trace evidence, idempotence, identity, backend state, approvals, validation and rollback before retrying.
Read articleA production runbook for qualifying AI agent memory with sources, traces, aging, permissions, evaluations, guardrails, human validation and rollback before it influences real actions.
Read articleA production runbook for qualifying an AI agent evaluation set with business cases, retrieval, tool calls, traces, thresholds, human validation and rollback before promotion.
Read articleA production runbook for qualifying Microsoft Foundry agent guardrails with sources, refusals, tools, identity, traces, canary, human validation and rollback before user exposure.
Read articleA production runbook for qualifying suspected prompt injection in an AI agent retrieval corpus with sources, traces, evaluation, tools, guardrails, validation and rollback.
Read articleA production runbook for qualifying an agent approval policy change with action scope, identity, traces, evaluation cases, guardrails, human validation and rollback.
Read articleA production runbook for qualifying Azure OpenAI or Microsoft Foundry throttling with quota, deployment capacity, agent traces, retries, fallback, validation and rollback before changing models.
Read articleA production runbook for rotating or revoking an AI agent runtime identity with scoped permissions, dry-run tool calls, traces, approvals, validation and rollback before breaking production actions.
Read articleA production runbook for qualifying a new AI agent tool with contract review, scoped identity, dry run, traces, approvals, evaluation cases and rollback before enabling real actions.
Read articleA production runbook for qualifying a retrieval index update with source diff, metadata, chunking, evaluations, traces, human validation and rollback before changing an AI agent's answers.
Read articleA production runbook for qualifying incomplete AI agent traces with conversation events, sources, tool calls, identities, approvals, evaluations and rollback before restoring an action.
Read articleA production runbook for qualifying AI agent contract drift with prompts, tool manifests, sources, evaluations, traces, human validation and rollback.
Read articleA runbook for validating an AI agent before real action by separating sources, tools, identity, evaluation cases, human approvals, logs and rollback.
Read articleA production runbook for exposing MCP tools to an AI agent while keeping action scope, approvals, identities, logs, evaluation and rollback under control.
Read articleA production runbook for qualifying an AI agent that selects the wrong tool, acts without evidence or hides an action behind a plausible answer.
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