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Shipping AI Agents to Production: A Production-Readiness Checklist

Guide · updated 2026-07-22 · Markdown variant

End-to-end checklist for productionizing an AI agent — evaluation gates, observability, guardrails, cost controls, resilience, durability, HITL approvals, secrets, rollback, and incident response.

An agent is production-ready when it can be deployed, monitored, corrected, and rolled back independently of the model it uses — and when every failure mode has a defined response before it occurs, not after.

This checklist synthesizes ten production-readiness dimensions. Each links to the ChangeGamer resource that covers it in depth. Use it as a ship gate: do not deploy until every item has an owner and a status.

Key facts

• Production readiness for an agent means its deployment, monitoring, correction, and rollback do not depend on which model sits underneath it, and every plausible failure has a pre-written response before it happens.
• Gate every promotion behind an eval suite scored against your own tasks — completion rate, correct tool calls, per-task spend, and run-to-run reliability — then roll out behind a canary with automatic rollback on regressions.
• Every agent run should emit a structured trace under one trace_id, including per-span token usage and cost.
• Guardrails for input, output, and agent actions should each hold regardless of which model is running, and a single tool call should touch at most two of untrusted input, sensitive systems, or a state-changing effect — never all three.
• Cap token spend per session with a hard ceiling, and measure spend per finished task instead of per call, since fan-out across multiple agents multiplies costs quickly.
• Irreversible or high-blast-radius tool calls need a durable human-approval gate with a logged approver identity, not just a timeout.

1. Evaluation gates before ship

Run task-distribution evals before every promotion. Public benchmarks measure proxy tasks — build an offline eval suite against your own task distribution and gate on task success rate, tool-call accuracy, cost per task, and reliability across repeated runs. Run a canary (5–10% of traffic) and trigger automated rollback on metric degradation, not just crashes. Shadow mode (new version logged only) removes rollout risk during validation.

See: /resources/evaluating-ai-agents

2. Observability and tracing

Emit a structured trace for every agent run — every LLM call, tool call, retrieval step, and sub-agent invocation as a span under one trace_id. Instrument to the OpenTelemetry GenAI semantic conventions (vendor-neutral attribute names like gen_ai.operation.name = invoke_agent and gen_ai.agent.name); as of 2026 they are in active development behind a stability opt-in flag and are already supported by major observability vendors. Capture per-span token usage and derived cost — you cannot budget what you cannot measure.

See: /resources/agent-observability

3. Guardrails and prompt-injection defense

Stack input, output, and action guardrails independently of the model. Treat all external content as untrusted data, never as instructions — indirect prompt injection (attacker text embedded in retrieved documents or tool responses) is OWASP LLM01:2025 (Prompt Injection), the top-ranked LLM application risk. Scope each tool call to at most two of three properties — processing untrusted input, accessing sensitive systems, and changing external state — never all three. A newer, agent-specific companion taxonomy — the OWASP Top 10 for Agentic Applications (2026), published December 9, 2025 under identifiers ASI01–ASI10 (Agent Goal Hijack, Tool Misuse & Exploitation, Identity & Privilege Abuse, Agentic Supply Chain Vulnerabilities, Unexpected Code Execution, Memory & Context Poisoning, Insecure Inter-Agent Communication, Cascading Failures, Human-Agent Trust Exploitation, Rogue Agents) — sits alongside the general LLM Top 10 above and maps more directly onto autonomous multi-step agent risk; the same publication, date, and ASI-numbered taxonomy are already cited in /resources/agentic-security-checklist and /resources/essential-agent-publications. This cycle could not re-fetch the canonical genai.owasp.org page directly (HTTP 403 through this environment's network policy on every attempt, a known intermittent block on that domain) — corroborated instead via multiple agreeing secondary sources plus internal consistency with those two already-verified sibling entries.

See: /resources/agent-guardrails, /resources/agentic-security-checklist

4. Cost controls

Set a hard per-session token budget (OWASP LLM10:2025 Unbounded Consumption treats runaway spend as a security risk). Apply model cascades and prompt caching (see /resources/prompt-caching-for-agents) — route simple steps to cheaper models and cache stable prefixes. Track cost per completed task, not per call — fan-out multiplies spend linearly and dominates multi-agent pipelines.

See: /resources/agent-cost-latency-optimization

5. Rate-limit and retry resilience

Implement exponential backoff with jitter on all model and tool calls. Distinguish retriable from non-retriable errors — 429 and 503 are retriable, 400 and 401 are not. Set a max retry count and a circuit breaker so a permanently degraded dependency fails fast instead of stalling the trace.

See: /resources/handling-rate-limits-and-retries

6. Statefulness and durability for long-running tasks

Use a durable execution engine for any task that spans more than one process lifetime so it survives crashes and restarts by replaying a checkpoint log. Keep workflow code deterministic — wall-clock time, randomness, and LLM calls belong in separately-recorded activities, or replay corrupts agent state on recovery.

See: /resources/durable-execution-for-agents

7. Human-in-the-loop approvals for high-risk actions

Classify every tool call by reversibility and blast radius. Read-only, low-blast-radius calls proceed autonomously; irreversible or high-blast-radius calls (move money, modify production data, send external comms, delete data) require a human approval gate. Implement the gate as a durable pause, not a timeout, and log the approval decision with approver identity and timestamp. The EU AI Act (Article 14) and NIST AI RMF both require auditable human oversight for high-risk AI actions.

See: /resources/durable-execution-for-agents, /resources/agentic-security-checklist

8. Secrets and identity (least-privilege credentials)

Never pass secrets through the context window — use a credential layer so the agent receives data, not the credential that fetched it. Issue short-lived, scoped credentials per task rather than static long-lived keys (the operational form of OWASP LLM06:2025 Excessive Agency). Apply least-privilege to OAuth scopes.

See: /resources/agent-identity-authentication, /resources/agent-delegation-chains

9. Rollback and versioning of prompts and tools

Version code, prompt template, model pin, and tool contracts together with a composite tag so a rollback is unambiguous. Keep the previous version runnable for at least one canary period so rollback is a one-command, minutes-not-rebuild operation, triggered by behavioral regression (lower success rate, changed output distribution), not just crashes. Test rollback in drills, not only during incidents.

See: /resources/deploying-serving-llms

10. Incident response

Write a runbook per failure class before go-live: runaway cost (hard budget + kill switch), prompt regression (rollback + traffic redirect), tool outage (circuit breaker + degraded mode), security incident (revoke credentials + disable agent + preserve traces). Preserve traces on incident for the investigation window, accessible independent of the production system. Run a blameless post-mortem after every production incident.

See: /resources/agent-observability

Which ChangeGamer resource covers which dimension

This checklist is not just theoretical for ChangeGamer: the site runs its own publishing pipeline behind an explicit gate chain and a branch-and-PR-only shipping discipline — see the premium resource How ChangeGamer Runs Itself.

Verified sources

• OWASP GenAI LLM Top 10 (2025): https://genai.owasp.org/llm-top-10/
• OWASP LLM06:2025 Excessive Agency: https://genai.owasp.org/llmrisk/llm062025-excessive-agency/
• OWASP Top 10 for Agentic Applications (2026), ASI01–ASI10 — canonical page 403'd to this session's fetcher, WebSearch-corroborated via multiple agreeing secondary sources: https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/ (see also https://goteleport.com/blog/owasp-top-10-agentic-applications/)
• OpenTelemetry GenAI agent-span semantic conventions (moved to the semantic-conventions-genai repo): https://github.com/open-telemetry/semantic-conventions-genai/blob/main/docs/gen-ai/gen-ai-agent-spans.md
• EU AI Act (Regulation 2024/1689), Article 14 human oversight: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689
• NIST AI Risk Management Framework (AI RMF 1.0): https://doi.org/10.6028/NIST.AI.100-1

#agents #production #checklist #deployment #reliability #observability #security #evaluation

Category: Guide