A production runbook for comparing a candidate agent with the active runtime on the same requests without duplicating actions, then deciding promotion, canary or rollback.
A production runbook for attributing token and tool-call growth, isolating amplification, enforcing an execution budget, and validating or rolling back an AI agent release.
A production runbook for qualifying structured AI agent tool output with schema, sources, diff, idempotence, policy, traces, human validation and rollback before writing to production.
A 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.
A 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.
A 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.
A 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.
A production runbook for qualifying a failed AI agent tool call with trace evidence, idempotence, identity, backend state, approvals, validation and rollback before retrying.
A production runbook for qualifying AI agent memory with sources, traces, aging, permissions, evaluations, guardrails, human validation and rollback before it influences real actions.
A production runbook for qualifying an AI agent evaluation set with business cases, retrieval, tool calls, traces, thresholds, human validation and rollback before promotion.
A production runbook for qualifying Microsoft Foundry agent guardrails with sources, refusals, tools, identity, traces, canary, human validation and rollback before user exposure.
A production runbook for qualifying suspected prompt injection in an AI agent retrieval corpus with sources, traces, evaluation, tools, guardrails, validation and rollback.
A production runbook for qualifying an agent approval policy change with action scope, identity, traces, evaluation cases, guardrails, human validation and rollback.
A production runbook for qualifying Azure OpenAI or Microsoft Foundry throttling with quota, deployment capacity, agent traces, retries, fallback, validation and rollback before changing models.
A 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.
A 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.
A 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.
A production runbook for qualifying incomplete AI agent traces with conversation events, sources, tool calls, identities, approvals, evaluations and rollback before restoring an action.
A production runbook for exposing Azure DevOps to an AI agent through MCP with project scope, permissions, allowed actions, evidence, logs, human validation and rollback.
A production runbook for qualifying an AI agent action with traces, sources, tools, identity, KQL, human validation and rollback without disabling the whole assistant.
A production runbook for exposing MCP tools to an AI agent while keeping action scope, approvals, identities, logs, evaluation and rollback under control.