Overture
A local-first AI delivery control plane that moves a product from prompt workshop and research through coordinated execution, testing, launch, and deployment.
01 / The problem
What needs to change
Turning an ambitious product brief into working software requires more than code generation: research, planning, coordination, quality, security, launch evidence, and deployment all need durable state and clear gates.
02 / The approach
A focused path through the complexity
Overture treats delivery as a guided lifecycle, preserving the plan as a canonical handoff while coordinating AI execution, artifacts, findings, runtime state, and operator-owned release decisions.
03 / Key capabilities
What the system is designed to do
- Prompt workshop and deep-research planning flow
- Structured milestone, dependency, and gate generation
- Multi-agent execution with resumable runtime state
- Project-level QA and security evidence
- Local launch and cloud deployment profiles
04 / Architecture
Technology and system shape
- Next.js control plane with SQLite persistence
- Codex-backed workshop, planning, and execution runtime
- Vendored Symphony orchestration with a tracker-compatible API
- Artifact storage, audit events, and per-project runtime workspaces
- Native, Docker, Azure, and AWS deployment paths
05 / Results
What the repository demonstrates
- The working application supports both a guided idea-to-deployment path and a fast path for an existing plan.
- Execution state, token use, findings, artifacts, gates, launch runs, and deployment evidence are visible from one control plane.
06 / Lessons
What the work teaches
- Long-running agent delivery needs explicit state, resumability, and evidence—not only a strong initial prompt.
- Release gates should expose partial or missing proof rather than silently treating tool availability as success.
07 / Screenshots
Approved visuals
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