How AI gets a feature right
AI doesn't get things right in one shot. ForgeaX uses an observable, traceable pipeline that guides each feature to a correct result, step by step.
A seven-step loop
Inside the engine, every feature goes through the same pipeline — requirements in, a working result out, with a readable record at each step:
- Requirements — state what to build, with acceptance criteria.
- Research — survey history, constraints and options.
- Plan — set a strategy and break it into tasks.
- Implement — write code and tests.
- Verify — independent checks; any red blocks the merge.
- Judgment — a human makes the final call.
- Finalize — merge once it passes.
An orchestrator and sub-agents
An "orchestrator" drives the state machine, deciding which sub-agent to dispatch at each step and checking its output; sub-agents are role-specialized with their own context. Humans inject judgment at just two points: stating requirements and reviewing results.
No "looks fine" slipping through
Verification has two AI gates: one statically scans docs and APIs from an AI user's perspective, catching "promised but not implemented"; the other actually runs the demo in an isolated sandbox, captures screenshots, and re-checks them — guarding against "tests green, screen black."
It gets steadier over time
After each feature, the friction encountered along the way is captured as feedback that upgrades the pipeline itself. The next feature automatically uses the improved version — the pipeline feeds on its own output and gets smarter.
The result: AI produces not "demo-grade" code, but verified, replayable features a human can take over.