AgentOps: contain sensitive data in agent traces before disabling observability
A production runbook for bounding a leak in agent traces, locating the unsafe field, canarying redaction and restoring useful observability.
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34 articles connected to this technical signal.
A production runbook for bounding a leak in agent traces, locating the unsafe field, canarying redaction and restoring useful observability.
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 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 detecting an agent delegation loop, containing side effects, rebuilding traces, and deciding controlled recovery or a single-agent rollback.
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 attributing token and tool-call growth, isolating amplification, enforcing an execution budget, and validating or rolling back an AI agent release.
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 AI agent permission drift with the real identity, RBAC, tool scopes, traces, expected denials, human validation and rollback before widening access.
Read articleA production runbook for qualifying the handoff from a Microsoft Foundry agent to Azure Automation, AWX, Azure DevOps or an MCP tool with contract, identity, traces, dry run, human validation and rollback.
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 an Azure DevOps failure on a self-hosted agent with disk, workspace, cache, local services, identity, logs, controlled cleanup, validation and rollback.
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 incomplete AI agent traces with conversation events, sources, tool calls, identities, approvals, evaluations and rollback before restoring an action.
Read articleA production runbook for exposing Azure DevOps to an AI agent through MCP with project scope, permissions, allowed actions, evidence, logs, human validation and rollback.
Read articleA production runbook for qualifying AI agent contract drift with prompts, tool manifests, sources, evaluations, traces, human validation and rollback.
Read articleA production runbook for qualifying an AI agent action with traces, sources, tools, identity, KQL, human validation and rollback without disabling the whole assistant.
Read articleA runbook for validating an AI agent before real action by separating sources, tools, identity, evaluation cases, human approvals, logs and rollback.
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