Writing
Mechanism-level pieces on agent reliability, AgentOps, MCP servers, and the harness that keeps AI systems honest in production.
· agent reliability · AgentOps · agent evaluation
Stop a verify–repair loop when evidence no longer supports another revision: preserve the champion, enforce a budget, and escalate when the verifier or calibration cannot support a reliable choice.
Read →· agent evaluation · AgentOps · agent reliability
Evaluate an AI agent reviewer as a chain: correct detection, critique uptake, accepted repair, and cost. Reviewer precision alone does not prove that the final answer improved.
Read →· MCP · security · agent reliability
Keep a private MCP server non-public while giving an agent a controlled connection path—then retain authorization, tool policy, auditability, and recovery at the server boundary.
Read →· agent workflows · AgentOps · orchestration
Agent loops remain useful for bounded work. When a system needs parallel branches, approval gates, checkpoints, and scoped recovery, make the control flow explicit with an agent workflow graph.
Read →· OpenAI · agent security · identity
Use OpenAI Workload Identity Federation to exchange an external workload identity for a short-lived access token, with a narrow service-account mapping and explicit permissions.
Read →· observability · agent reliability · AgentOps
An agent observability dashboard should show run state, tool behavior, retry pressure, and spend together so operators can explain the workflow before they intervene.
Read →· agent reliability · AI readiness · AgentOps
An AI agent rollout checklist should prove scope, tool safety, recovery, review, telemetry, and cost controls before the workflow runs without a human watching every step.
Read →· AgentOps · incident response · agent reliability
AgentOps incident response starts by freezing side effects, preserving state, classifying the failure, and choosing a recovery path before the agent resumes.
Read →· agent reliability · observability · failure modes
AI agents fail in production for boring harness reasons: vague tool contracts, lost state, bad retries, and missing observability.
Read →· MCP · tools · production
A production MCP server survives when tools, resources, authorization, and logging are explicit and the server owns the audit trail.
Read →· AgentOps · agent reliability · SRE
Treat production AI agents like SRE systems: checkpoints, idempotent writes, retry budgets, observability, and cost guards around the model.
Read →· agent reliability · governance · approvals
Human approval gates work when the harness freezes the exact action surface, preserves the reviewer decision, and invalidates stale approvals before resume.
Read →· agent reliability · recovery · checkpoints
An agent workflow recovery plan reconciles checkpoints, side effects, and approval state before a resumed run is allowed to act again.
Read →· AgentOps · agent reliability · SLOs
Agent reliability SLOs should measure finished work, recovery behavior, escalation, and cost per accepted output rather than model latency alone.
Read →· cost controls · agent reliability · runbooks
A cost runbook classifies the loop, contains the damage, and measures accepted output so routing and retry controls get tuned for the right failure mode.
Read →· agent reliability · governance · controls
A kill switch should pause the workflow, preserve the last safe checkpoint, and hand the decision back to a human before the agent keeps acting.
Read →· agent reliability · tool contracts · idempotency
A production agent tool contract separates intent, validation, execution, and audit so retries stay legible and side effects stay bounded.
Read →· agent reliability · AI readiness · scoping
An AI agent scope audit chooses the first workflow by recoverability: clear ownership, bounded tools, observable progress, and reviewable side effects.
Read →· AgentOps · agent reliability · services
AgentOps hardening services inspect workflow contracts, tool safety, recovery behavior, telemetry, and review boundaries before a production agent runs unattended.
Read →· cost controls · agent reliability · routing
Multi-model routing controls cost by making the harness choose the cheapest reliable lane for each agent step, then measuring accepted output instead of raw call spend.
Read →· agent reliability · idempotency · MCP
Make tool calls idempotent by separating intent, side effect, and acknowledgement — so a retry never double-charges, double-sends, or double-writes.
Read →· AgentOps · agent reliability · production AI
AgentOps is the discipline of making AI agents reliable in production — checkpoints, idempotent tool calls, retry budgets, cost guards, and observability. The mechanism, the metrics, and a hardening checklist.
Read →