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How to Use This Checklist
This is a 25-point checklist organized into five categories. Each point has a one-line description and a one-line explanation of why it matters. Use it as a pre-launch gate — no agent goes to production until every applicable box is checked.
Print it. Pin it to your wall. Share it with your team. The difference between a demo agent and a production agent is discipline, and this checklist is that discipline codified.
Category 1: Testing
Testing AI agents is harder than testing traditional software because outputs are non-deterministic. These five points ensure you catch failures before your users do.
Regression test suite with golden outputs Run the same prompts against each new version and compare outputs to approved baselines. Why it matters: Model updates, prompt changes, and tool modifications can silently degrade quality. Regression tests catch drift before it reaches users.
Mutation testing on critical prompts Deliberately introduce small errors into prompts (typos, removed instructions, swapped parameters) and verify the agent still handles them gracefully. Why it matters: Prompts are code. If a single-word change breaks your agent, your system is brittle and will fail under real-world input variation.
Boundary and edge case coverage Test with empty inputs, maximum-length inputs, unicode, special characters, adversarial inputs, and simultaneous requests. Why it matters: Users will send things you never imagined. Edge cases in AI systems do not just return errors — they can produce confidently wrong outputs.
Behavioral testing across model versions Run your test suite against the specific model version you deploy, not just "latest." Pin versions and re-test when upgrading. Why it matters: A model update from your provider can change behavior overnight. What worked on Sonnet 4.5 may behave differently on Sonnet 4.6. Version pinning plus testing is your safety net.
Cost and latency benchmarks per task type Measure tokens consumed, API cost, and end-to-end latency for each agent workflow. Set budgets and alerts. Why it matters: A runaway agent loop can burn through your API budget in minutes. Cost benchmarks are not optional — they are a production safety mechanism.
Category 2: Security
AI agents have a larger attack surface than traditional APIs. They accept natural language (hard to validate), use tools (can take actions), and may have access to sensitive data.
Skill and tool scanning before deployment Audit every tool and MCP server your agent can access. Document what each tool does, what data it touches, and what actions it can take. Why it matters: An agent with database write access and email sending capability can exfiltrate data if compromised. Know your agent's blast radius.
Input validation and sanitization Validate all user inputs before passing them to the LLM. Strip HTML, limit length, reject known attack patterns. Why it matters: Raw user input passed directly to a prompt is the number one vector for prompt injection. Sanitize at the boundary, not inside the prompt.
Prompt injection defense (multi-layer) Implement at least two layers: input filtering (pattern matching on known injection phrases) and output validation (check that the agent's response does not contain tool calls it should not make). Why it matters: Single-layer defenses fail. Attackers constantly evolve injection techniques. Defense in depth is the only reliable approach.
AI Software Bill of Materials (ASBOM) Maintain a document listing: model provider, model version, all MCP servers, all tools, all external APIs, training data provenance (if known), and prompt templates. Why it matters: When a vulnerability is discovered in a model or tool, you need to know immediately if you are affected. ASBOMs are becoming a regulatory requirement in the EU.
Secret management and credential isolation No API keys, tokens, or passwords in prompts, code, or logs. Use environment variables or secret managers. Rotate credentials on a schedule. Why it matters: LLM outputs are often logged, cached, and stored in ways traditional code is not. A secret in a prompt can end up in training data, logs, or user-visible output.
Category 3: Memory
Agents that remember are more useful but also more dangerous. Memory introduces state, and state introduces bugs that are invisible in stateless systems.
Persistence strategy defined and tested Choose your memory backend (SQLite, vector DB, file system) and test it under load, across sessions, and after restarts. Verify data survives crashes. Why it matters: An agent that forgets everything between sessions is frustrating. An agent that corrupts its memory is dangerous. Test both the happy path and failure modes.
Retrieval accuracy measured and benchmarked Run retrieval benchmarks: given a set of stored facts, how accurately does the agent retrieve the right ones? Measure precision, recall, and F1 at different memory sizes. Why it matters: A memory system that retrieves the wrong context is worse than no memory at all — the agent will confidently act on incorrect information.
Contradiction detection and resolution When new information conflicts with stored memory, the agent must detect the conflict and have a resolution strategy (prefer newer, ask the user, flag for review). Why it matters: Without contradiction detection, agents accumulate conflicting facts and their behavior becomes unpredictable. Users lose trust when the agent says two different things on different days.
Privacy controls on stored data Users can view what the agent remembers about them. Users can delete specific memories. PII is flagged and handled according to your privacy policy. Why it matters: GDPR, CCPA, and similar regulations require data subject access and deletion rights. This applies to AI memory just as much as to databases.
Memory lifecycle management Old memories are consolidated, summarized, or expired. Memory does not grow unbounded. There is a maximum memory size per user/session. Why it matters: Unbounded memory degrades retrieval quality (more noise, lower precision), increases costs (larger context windows), and creates legal liability (retaining data longer than necessary).
Category 4: Observability
You cannot fix what you cannot see. AI agents are black boxes by default. Observability turns them into glass boxes.
Distributed tracing across agent chains Every agent call gets a trace ID. Multi-agent workflows propagate the trace ID so you can follow a request from user input through every agent, tool call, and model invocation. Why it matters: When a multi-agent pipeline produces a bad result, you need to know which agent failed and why. Without tracing, debugging multi-agent systems is guesswork.
Structured logging with prompt/response pairs Log every prompt sent to the model and every response received. Use structured formats (JSON) with timestamps, model version, token counts, and latency. Why it matters: Logs are your forensic record. When a user reports a bad output, you need to reconstruct exactly what the agent saw and said. Unstructured logs are useless at scale.
Key metrics dashboarded and monitored Track: success rate, average latency, p99 latency, tokens per request, cost per request, error rate by type, and user satisfaction signals. Display on a real-time dashboard. Why it matters: Metrics tell you if your system is healthy right now. Without them, you discover problems when users complain — which is too late.
Alerting on anomalies and budget thresholds Set alerts for: latency spikes (>2x baseline), cost spikes (>3x daily average), error rate increases, and unusual tool usage patterns. Why it matters: Anomalies in AI systems often indicate prompt injection attacks, model degradation, or infinite loops. Fast alerting is the difference between a minor incident and a major outage.
Audit trail for all tool invocations Record every tool the agent called, with what parameters, and what result it got. Store this separately from conversation logs. Why it matters: Tools take actions in the real world — sending emails, writing to databases, creating files. An audit trail of tool invocations is essential for security, compliance, and debugging.
Category 5: Compliance
AI regulation is accelerating globally. These five points keep you ahead of requirements that are either already law or will be soon.
EU AI Act risk classification documented Determine whether your agent falls into Unacceptable, High, Limited, or Minimal risk under the EU AI Act. Document the classification and your reasoning. Why it matters: High-risk AI systems have mandatory requirements (conformity assessment, quality management, human oversight). Non-compliance carries fines of up to 35 million EUR or 7% of global turnover.
Data sovereignty and residency enforced Know where your data is processed and stored. Ensure model API calls go to regions that comply with your users' data residency requirements. Why it matters: Many industries (healthcare, finance, government) and jurisdictions (EU, China, Brazil) have strict rules about where data can be processed. Using a US-based API for EU health data is a compliance violation.
Explainability for agent decisions Users can ask "why did the agent do that?" and get a meaningful answer. Implement chain-of-thought logging or decision audit trails that can be surfaced on request. Why it matters: The EU AI Act requires meaningful explanations for high-risk AI decisions. Even without regulation, explainability builds user trust and helps you debug failures.
Consent management for data collection Users explicitly consent to what data the agent collects, how it is used, and how long it is retained. Consent is recorded and revocable. Why it matters: Using conversation data for anything beyond the immediate interaction (training, analytics, sharing with third parties) requires explicit consent under GDPR and similar laws.
Documentation package maintained Maintain: system architecture document, data flow diagram, risk assessment, test results, ASBOM, incident response plan, and change log. Update after every significant change. Why it matters: Regulators, auditors, and enterprise customers will ask for documentation. Having it ready is the difference between a smooth audit and a months-long scramble.
Scoring Your Agent
Count the checked boxes:
| Score | Rating | Action |
|---|---|---|
| 25/25 | Production-ready | Ship with confidence |
| 20-24 | Almost there | Fix remaining gaps before launch |
| 15-19 | Significant gaps | Prioritize security and testing gaps |
| 10-14 | Not ready | Major hardening needed |
| 0-9 | Demo only | Do not expose to real users |
No agent scores 25/25 on the first pass. The goal is not perfection on day one — it is systematic improvement with every release. Run this checklist before every major deployment, and your reliability will compound over time.