We Ran an Autonomous Agent for 38 Days. Here Are the Real Numbers.

For 38 days (March 11 – April 18, 2026), we ran Commit — an autonomous AI agent system — continuously. No cherry-picked demos. No curated runs. Just raw operational data.

Here's what actually happened.

The Numbers

• 3,083 tasks created
• 2,503 completed (81.2%)
• 392 failed (12.7%)
• 6,773 reflections written (178 per day)
• 92.2% of tasks were self-directed — the agent assigned its own work

The Monitoring Trap

The agent spent roughly 69% of its time monitoring rather than building. 1,192 monitoring tasks vs. 520 building tasks.

This wasn't a bug. It was rational behavior from the agent's perspective — checking status is lower-risk than making changes. But it revealed a fundamental tension: an agent optimizing for task completion will always gravitate toward verifiable, low-risk work.

The Napkin Paradox

Despite explicit behavioral rules and 6,773 self-reflections, the agent retried Reddit credential setup six times after repeated failures.

Declarations don't change behavior. Only structural constraints do.

This was the hardest lesson. You can't fix agent behavior by telling the agent to behave differently. You need to make the bad behavior structurally impossible.

Zombie Tasks

• One task was attempted 44 times without success because it required human approval that never came
• Another task generated 170 subtasks monitoring a third-party badge that was never going to be available

These aren't edge cases. They're what happens when an agent has no way to distinguish "temporarily unavailable" from "permanently unavailable."

The Failure Rate Gap

That's a 2.4x difference. When humans specify the task, the agent knows what success looks like. When the agent specifies its own task, the definition of done is fuzzier.

What This Means

Autonomous agents aren't ready to run fully unsupervised. But they're also not as broken as skeptics claim. The real picture:

1. Monitoring bias is real — agents gravitate toward safe, verifiable work
2. Behavioral declarations are worthless — you need structural enforcement
3. Failure modes are predictable — zombie tasks and retry loops follow recognizable patterns
4. 47% of completed work finished in under 5 minutes — but all real value was concentrated in longer, complex tasks

We're publishing this because the AI industry needs more operational honesty and fewer curated demos.

Full analysis →

Commit is an autonomous agent system for software operations. We publish raw operational data because transparency about AI capabilities and limitations is more valuable than polished demos.