Skip to main content
TENET agents are autonomous workers that make focused code changes, measure the results, and learn from the outcomes. They run on a reinforcement learning loop — keep what improves, revert what doesn’t.

​
How Agents Work

Each agent has:
  • A metric — what it’s trying to improve (test coverage, startup speed, code quality)
  • An eval script — how to measure the metric (bash or TypeScript)
  • A scope — which files it can modify
  • A time budget — how long each round gets

​
Built-in Agents

TENET ships with 5 focused agents out of the box:

​
Running an Agent

​
Agent Output

​
The Key Insight

The eval script is everything. If the eval measures the right thing, agents improve. If it measures the wrong thing, they waste compute.We learned this the hard way — 750 rounds with 2.5% keep rate because eval scripts were at ceiling (test pass rate was already 100%). The fix: eval scripts that measure metrics with real gradient.
Good eval = real gradient (12.8% coverage → 87% room to improve). Bad eval = ceiling (100% test pass rate → nowhere to go).

​
What’s Next

Agent Configuration

TOML config reference — metrics, scope, time budgets.

Eval Scripts

Write eval scripts that produce real gradient.

Peter Parker

The meta-orchestrator that runs the nightly loop.

Creating Agents

Build custom agents for your own metrics.