Why This Checklist Exists

AI agents are moving from demos to production. But most frameworks are optimized for prototyping, not reliability.

This checklist comes from real production deployments, the failures, and the incidents. It’s what I wish I had before deploying my first agent to a live system.

Use this before deploying any AI agent that:

• Modifies state (databases, APIs, files)
• Handles money (payments, refunds, billing)
• Sends communications (emails, SMS, notifications)
• Makes decisions with business impact

If you can answer YES to everything here, your agent is probably production-ready. If not, you know exactly what needs fixing.

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1. Safety & Guardrails

Can your agent safely fail?

[ ] Every state-modifying operation has a rollback procedure

• Example: charge_customer has corresponding refund_customer
• Rollbacks execute automatically on downstream failures
• Rollbacks are tested and verified to work

[ ] High-risk operations require human approval

• Financial transactions above threshold
• Data deletion or modification
• External communications to customers
• Clear approval workflow with full context shown to approver

[ ] Agent has hard budget limits

• Maximum API tokens per execution
• Maximum execution time (timeout)
• Maximum API calls per minute/hour
• Budget violations halt execution, don’t just log

[ ] Agent cannot access forbidden resources

• Production databases are read-only or require approval
• Customer PII is redacted in logs
• Secrets are not exposed to LLM prompts
• File system access is sandboxed to specific directories

[ ] Critical operations are idempotent

• Retrying the same operation produces the same result
• No duplicate charges, emails, or state changes
• Request IDs or transaction IDs prevent duplicates

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2. Observability & Debugging

Can you debug a failure in under 60 seconds?

[ ] Every LLM call is logged with full context

• Complete prompt sent to LLM
• Complete response received
• Model name and parameters
• Timestamp and execution time
• Cost in tokens and dollars

[ ] Every tool execution is traced

• Tool name and parameters
• Return value or error
• Execution time
• Success/failure status
• Stack trace on errors

[ ] You can replay any execution from logs

• Stored traces include enough information to reproduce the execution
• You can step through agent reasoning (ReAct pattern)
• You can see why the LLM chose each action

[ ] You can query failures efficiently

• Filter by: status, tool name, date range, execution time
• Find patterns: “Which tools fail most often?”
• Export for analysis: CSV, JSON, or database format

[ ] Logs are structured, not just text

• JSON or JSONL format
• Consistent schema across executions
• Queryable with standard tools (jq, SQL, Elasticsearch)

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3. Reliability & Resilience

Does your agent recover gracefully from failures?

[ ] Transient failures trigger automatic retries

• Network timeouts retry with exponential backoff
• Rate limit errors wait and retry
• Configurable max retries per operation
• Different retry policies for different tools

[ ] Partial failures trigger automatic rollback

• If step 3 fails, steps 2 and 1 are undone
• Compensating transactions restore consistent state
• Rollback failures are logged and alerted

[ ] Critical paths have timeout protection

• No operation runs indefinitely
• Timeout values are tuned per operation type
• Timeouts trigger rollback, not just errors

[ ] System degrades gracefully when dependencies fail

• Non-critical tools can fail without stopping workflow
• Agent provides partial results when possible
• Clear error messages explain what failed and why

[ ] Concurrent executions don’t interfere

• Multiple agent runs don’t corrupt shared state
• Lock mechanisms prevent race conditions
• Workflow isolation is tested

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4. Compliance & Audit

Can you prove what your agent did and why?

[ ] Full audit trail of agent decisions

• Who/what triggered the execution
• Every decision point with LLM reasoning
• Every action taken and its result
• Timestamps for entire chain of events

[ ] Human-in-the-loop for regulated operations

• GDPR data deletion requires approval
• Financial transactions require review
• Approval records stored permanently
• Approver identity and timestamp logged

[ ] Cost tracking per execution

• Total API cost (tokens × price per token)
• Per-model cost breakdown
• Cost alerts when budget is exceeded
• Historical cost trends

[ ] Reproducible execution from stored traces

• Exact LLM version and parameters recorded
• Tool versions and dependencies captured
• Can reproduce execution for compliance review
• Traces stored for required retention period

[ ] Sensitive data is handled correctly

• PII is redacted in logs
• Secrets never appear in traces
• Compliance with GDPR, CCPA, etc.
• Data retention policies enforced

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5. Testing & Validation

Have you tested what happens when things break?

[ ] Unit tests for every tool

• Test success cases
• Test error cases
• Test edge cases (empty inputs, null values)
• Mock external dependencies

[ ] Integration tests for multi-step workflows

• Test complete end-to-end flows
• Test with real LLM (not mocked)
• Test with production-like data
• Verify rollback works correctly

[ ] Failure injection tests

• What if step 3 fails? Does rollback work?
• What if external API is down?
• What if LLM returns malformed response?
• What if network timeout occurs?

[ ] Cost estimation before production

• Run workflow with test data
• Measure token usage and API costs
• Project costs at production scale
• Set up cost alerts

[ ] Load testing for expected traffic

• Can system handle peak load?
• Are rate limits respected?
• Does performance degrade gracefully?
• Are bottlenecks identified?

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6. Monitoring & Alerting

Will you know immediately when something goes wrong?

[ ] Real-time alerts for critical failures

• Agent execution failures
• Budget overruns
• Rollback failures
• Suspicious patterns (unusual cost, repeated failures)

[ ] Dashboards for operational visibility

• Success/failure rates
• Average execution time
• Cost trends over time
• Most common failure modes

[ ] On-call runbooks for common issues

• What to do when agent fails
• How to manually trigger rollback
• How to disable agent in emergency
• Escalation paths for different failure types

[ ] Regular review of agent behavior

• Weekly review of failure patterns
• Monthly cost analysis
• Quarterly security audit
• Continuous improvement based on incidents

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7. Human Oversight

Can humans intervene when needed?

[ ] Clear mechanism to pause/stop execution

• Emergency kill switch
• Graceful shutdown (finish current step, then stop)
• Preserve state for later resumption

[ ] Ability to manually override decisions

• Human can approve/reject specific actions
• Override decisions are logged
• Override doesn’t break workflow

[ ] Escalation paths for edge cases

• Agent can ask for human help
• Clear SLAs for human response time
• Workflow pauses until human responds

[ ] Regular human review of agent outputs

• Spot checks of decisions
• Review of edge cases
• Validation that agent behavior matches intent

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8. Deployment & Operations

Is your deployment process safe and repeatable?

[ ] Staging environment for testing

• Separate from production
• Representative data (but not real customer data)
• Test all changes in staging first

[ ] Gradual rollout strategy

• Deploy to 1% of traffic first
• Monitor for issues before full rollout
• Easy rollback if problems detected

[ ] Version control for prompts and tools

• Prompts are versioned
• Tool definitions are versioned
• Can roll back to previous versions

[ ] Clear deployment documentation

• Step-by-step deployment guide
• Rollback procedures
• Contact information for incidents

[ ] Post-deployment validation

• Smoke tests run automatically
• Verify agent is working as expected
• Alert if smoke tests fail

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Implementation Guide

Using AgentHelm for Production Readiness

AgentHelm was built specifically to help you check these boxes. Here’s how it maps to the checklist:

Safety & Guardrails:

@tool(
requires_approval=True,  # Human approval for high-risk ops
compensate_with=rollback_action,  # Automatic rollback
max_retries=3,  # Retry transient failures
timeout=30.0  # Hard timeout
)
def risky_operation(data: dict):
return api.modify(data)

Observability:

# Query failures
agenthelm traces filter --status failed --date-from 2025-11-01

# Export for analysis
agenthelm traces export --output report.csv --format csv

# Replay execution
agenthelm traces show <trace_id>

Reliability:

• Compensating transactions for rollback
• Automatic retry with exponential backoff
• Timeout protection per tool
• SQLite/JSON storage for traces

Compliance:

• Full audit trail with LLM reasoning
• Approval records with timestamps
• Cost tracking per execution
• Reproducible from stored traces

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Real-World Example: Customer Refund Agent

Here’s how this checklist applies to a real production agent:

from agenthelm import tool

# ✅ Safety: Rollback defined
# ✅ Observability: Automatically logged
# ✅ Reliability: Retries configured
@tool(
compensate_with=log_verification_failure,
max_retries=2
)
def verify_order(order_id: str) -> dict:
"""Step 1: Verify order exists and is refundable"""
return orders_api.verify(order_id)

# ✅ Safety: Requires approval, has rollback
# ✅ Compliance: Approval logged with identity
@tool(
requires_approval=True,  # Manager approves refunds >$100
compensate_with=reverse_refund,
timeout=30.0
)
def process_refund(order_id: str, amount: float) -> dict:
"""Step 2: Issue refund to customer"""
return payments_api.refund(order_id, amount)

@tool()
def reverse_refund(order_id: str, amount: float) -> dict:
"""Rollback: Re-charge customer if needed"""
return payments_api.charge(order_id, amount)

# ✅ Reliability: Has rollback for email failure
@tool(compensate_with=send_cancellation_email)
def send_refund_email(customer_email: str, amount: float):
"""Step 3: Notify customer"""
return email_api.send(customer_email, f"Refund of ${amount} processed")

What this checklist ensures:

• If email fails → refund reversed → customer re-charged → consistent state
• If refund >$100 → human approval required → logged with timestamp
• Every LLM decision → stored in SQLite → queryable for compliance
• Network timeout → automatic retry → exponential backoff
• Complete failure → full rollback → audit trail preserved

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Your Next Steps

1. Print this checklist
2. Go through it for your agent - Honestly mark YES/NO for each item
3. Fix the NOs - Start with safety, then observability, then reliability
4. Test failure scenarios - Inject failures and verify rollback works
5. Deploy to staging - Run for 1 week, monitor closely
6. Gradual production rollout - 1%, 10%, 50%, 100%

If you can’t check all these boxes, you’re not ready for production. That’s not a judgment but it’s a fact. Production agents that modify real state need the same rigor as traditional software.

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Contributing to This Checklist

This checklist is a living document based on real production experience. If you’ve deployed agents in production and learned lessons not captured here, please contribute:

• Add items: What’s missing from this checklist?
• Share war stories: What failures have you seen?
• Improve examples: How can this be more practical?

GitHub: https://github.com/hadywalied/agenthelm/blob/main/CHECKLIST.md GitHub Discussion: https://github.com/hadywalied/agenthelm/discussions/12

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Conclusion

Production AI agents are not demos. They need:

• Transactional safety (rollback on failure)
• observability (debug in seconds, not hours)
• Human oversight (approval for high-risk operations)
• Operational discipline (monitoring, alerting, incident response)

This checklist captures what I’ve learned deploying agents. Use it to avoid the mistakes I made.

The goal isn’t perfection. The goal is production-readiness.

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• GitHub: github.com/hadywalied
• LinkedIn: linkedin.com/in/hadywalied
• AgentHelm: github.com/hadywalied/agenthelm