LoopPilot AI runs a self-correcting loop of five specialist agents. Give it a goal — it plans, ships, tests and rewrites itself until the result actually works.
Each stage feeds the next. When the loop can't reach the quality bar, it rewrites itself and tries again.
Describe the outcome you want in plain language.
Planner decomposes the goal into a dependency-aware roadmap.
Builder executes the plan step by step.
Tester validates each step and captures signals.
Reviewer scores quality and reasons about failure modes.
Optimizer patches the plan and re-runs the loop.
Loop converges when quality clears the target.
Every capability you'd expect from a serious AI engineering platform — with the transparency you'd expect from your own team.
Turns fuzzy goals into a concrete, dependency-aware plan without hand-holding.
Planner, Builder, Tester, Reviewer and Optimizer negotiate outcomes in parallel.
Every failure feeds the next iteration. The loop tightens itself.
See every plan, action, and observation as it happens — no black boxes.
Structured post-mortems: what went well, what broke, what to change.
Confidence, quality and ETA update in real time as agents work.
The loop remembers past runs, preferences, and conventions.
Every iteration is graded so convergence is measurable, not vibes.
Autonomous loops compress days of planning, coding and review into a single continuous run.
Every change is tested, scored and reflected on before the loop advances.
Transparent decisions, editable plans, and full audit trails — never a black box.
Launch LoopPilot and watch five agents plan, ship, test and improve — until the goal is done.