Tenet

Tenet
Talk. Establish. Nonstop. Evaluate. Tenet.

The agent harness for long, reliable autonomous development.

AI coding agents are powerful but short-lived — they lose context, drift off-spec, skip tests, and can't hold a multi-hour session together. Tenet is the harness around them: it interviews you, writes the spec, plans the work as a dependency graph, adapts that plan to your model's capability, executes each job, and judges every job with independent critics before moving on. Runs loop for hours; the project's durable truth keeps itself current between them.

*tenet* — a principle held to be true. Also a palindrome: it reads the same forward and backward, just like the process. Each cycle produces a tenet — a verified feature, and a truer project — that feeds the next.

Why Tenet?

- Plans that adapt to your model. Declare frontier or local and Tenet reshapes the DAG to match — local models get a finer-grained plan with explicit per-job acceptance criteria; frontier models get a coarse, goal-oriented one. Every worker receives the spec inlined, so no one works blind. Run cheaper models without drifting off-spec.
- Independent, adversarial critics. Every job faces critics with fresh context and no access to the author's reasoning. Grounded critics check conformance to the spec; ungrounded critics review free of it, catching issues the spec itself missed. The oracle problem — AI tests that verify implementation behavior rather than intent — is checked explicitly.
- Self-correcting project doctrine. Durable project truth (architecture, testing, design) stays current on its own: jobs flag stale doctrine as drift, the run consolidates proposals, and an authorized job applies the accepted ones. Your .tenet/project/ never silently rots.
- DAG-based orchestration.…