Lessons from Six Months of Running Agents in Production

Six months ago, I deployed my first production AI agent. It was supposed to handle a straightforward task—monitoring data pipelines and alerting on anomalies. Simple enough, right?

What I've learned since then has completely changed how I think about building and deploying autonomous systems. Here are the lessons that actually mattered.

Lesson 1: Expect the Unexpected

Within the first week, my "simple" monitoring agent had:

• Sent 47 alerts for a single incident because it didn't recognize duplicate issues
• Tried to page me at 3 AM for a warning that could have waited until morning
• Missed an actual critical failure because it was too busy with the false alarms

The lesson: agents encounter situations you didn't anticipate. Build for that reality from day one, not as an afterthought.

Lesson 2: Observability Is Not Optional

I started with minimal logging. Big mistake. When things went wrong—and they did—I had no idea why. Now I log:

• Every decision the agent makes and why
• What alternatives it considered
• What context it had at decision time
• How confident it was
• What actually happened as a result

Lesson 3: Agents Drift

Over time, my agent started behaving differently. Not because the code changed, but because:

• The data it was monitoring evolved
• Edge cases became more common
• Accumulated context subtly shifted its behavior

I now have regression tests that run weekly to catch drift before it becomes a problem.

Lesson 4: Humans Will Circumvent Bad UX

When the agent was annoying, people found ways to work around it:

• Muting its alerts entirely
• Creating separate channels it couldn't see
• Just ignoring its output

The fix wasn't better agent logic—it was better integration with how people actually worked. I had to design the human-agent interaction, not just the agent.

Lesson 5: Cost Control Is a Feature

My first month's API bill was... educational. Agents are surprisingly good at burning tokens when left unchecked.

• Month 1: No limits, massive overruns
• Month 2: Hard limits, agents hitting walls
• Month 3+: Smart budgets with graceful degradation

Cost control isn't about being cheap—it's about sustainability. An agent that's too expensive to run won't run for long.

Lesson 6: The 80/20 Rule Applies

80% of tasks are straightforward. 20% are edge cases that consume 80% of your debugging time.

I stopped trying to make the agent perfect on everything. Instead:

• It handles routine cases autonomously
• It escalates edge cases to humans
• It learns from human resolutions

This pattern—autonomy within bounds—works better than trying to solve everything.

Lesson 7: Maintenance Is Ongoing

Traditional software: ship it and maybe patch bugs. Agents: continuous tuning and adjustment.

• Weekly reviews of agent behavior
• Monthly tuning of thresholds and parameters
• Quarterly reassessment of scope and capabilities

Plan for this maintenance burden. It's not optional.

Lesson 8: Simple Beats Clever

I built increasingly sophisticated agents that did increasingly worse. Complexity breeds fragility.

Now I follow a rule: if I can't explain how an agent makes a decision in one sentence, it's too complex. Simple agents with clear logic outperform clever agents with opaque reasoning.

Lesson 9: Trust Is Earned Incrementally

You don't deploy an agent and trust it immediately. Trust builds over time:

• Phase 1: Agent runs in shadow mode, humans review everything
• Phase 2: Agent acts with human approval
• Phase 3: Agent acts autonomously for low-stakes decisions
• Phase 4: Gradual expansion of autonomous scope

Skip phases and you'll regret it. Trust requires evidence.

Lesson 10: Documentation Is for Future You

Six months later, I've forgotten why half the decisions were made. The only things that saved me:

• Detailed runbooks for common issues
• Decision logs explaining why choices were made
• Architecture docs that stayed updated

Future you will not remember. Document accordingly.

What I'd Do Differently

If I were starting over:

• Start with half the scope I think I need
• Build observability from day one
• Plan for drift and regression
• Design human-agent interaction deliberately
• Set up cost controls before first deployment
• Assume edge cases will dominate my time

The Good News

Despite all these lessons learned the hard way, the agent has been genuinely valuable. It catches issues I would have missed, handles routine work so I can focus on harder problems, and has become a reliable part of the team.

Production agents are worth the effort. Just go in with your eyes open.

What lessons have you learned from running agents in production? I'm collecting war stories at matt@emmons.club.