From natural-language intent
to deployed, governed software.
The Judge-Builder-Worker loop is a daemon-driven iteration engine that replaces prompt-and-pray with structured, criteria-driven development cycles — complete with cost tracking, stuck detection and human escalation.
Traditional AI coding tools give you one shot — you prompt, they answer, and you hope. Crucible inverts that. A long-running daemon orchestrates three specialist agents in a continuous loop: a Builder composes the next set of phases, a Worker executes them, and a Judge evaluates the outcome against your success criteria. The loop only stops when the business requirements are met, the work is blocked on a human, or policy says to stop.
What it is, in plain terms.
The three-agent loop
Builder composes. Worker executes. Judge evaluates. Evidence flows between them as structured handoff documents — not ad-hoc chat logs.
Real-time observability
Live stdout, phase timing, iteration costs, criteria progress and handoff diffs — streamed to a dashboard you can watch like a game.
Deterministic stuck detection
Composition fingerprinting, diff-size trends and criteria stagnation analysis catch loops that are spinning without making progress.
Evidence-rich handoffs
Every iteration preserves file changes, test results, verification reports and cost — so humans can inspect any decision after the fact.
What changes for the business.
The loop runs as a daemon. It doesn’t sleep. It doesn’t lose context. It resumes from crash. It makes progress while your team is at dinner.
What traditional engineering books as a quarter of capacity, Crucible collapses into hours of expert-led iteration.
Cost isn’t a mystery. Every iteration has a budget, a running total, and a hard stop. No surprise AI invoices.
Enterprise-grade by default.
Goal-oriented phase composition
The Builder doesn’t micromanage. It composes the next phase of work — research, build, test, verify — targeting the outstanding criteria.
Delta-mode verification
After iteration one, judges only re-evaluate unmet criteria. Token spend stays sane even on long, complex projects.
Crash-safe daemon
Daemon restarts don’t lose progress. State, handoffs, git history and criteria status persist across process boundaries.
Deterministic escalation paths
Budget exhausted, iteration cap reached, stuck detected, policy violation — each has a defined, human-visible outcome.
Auto-commit per phase
Every phase ends with a clean git commit. You can replay, revert and review the build like any other source tree.
Permission-driven guardrails
What a user can build in the loop is bounded by their Archon role. An intern ships to sandbox; a platform admin ships to prod.
The loop is the proof that AI can be trusted with engineering.
The Inversion Principle demands that AI do the work while humans stay in charge of intent. But that only works if the work AI does is inspectable and reversible.
Crucible’s orchestration loop is designed from first principles to keep that inspection possible. Every phase has a criterion. Every iteration has an artifact. Every handoff has a diff. Every decision has a cost. The result is a software-development process humans can delegate to AI — because the trail of evidence makes the delegation safe.
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