Traycer AI

Generative coding agents are powerful, but without orchestration, they can drift from intent, over‑edit, or break things. Traycer solves this by planning first, executing deliberately, and proving outcomes.

Traycer works with the user to turn an objective into sequenced phases - essentially mini-prompts. It’s an interactive process where the Agent may ask for clarification and develop high-level phases. The user can provide their input to fine-tune and adjust the Phases. Traycer works on each Phase to come up with a detailed plan with file-by-file instructions covering low-level details about classes, variables, and call hierarchies. Each phase is then dispatched to the right agent (your choice) for code generation. After each handoff, Traycer runs verification—acceptance checks tied to your intent, tests, static analysis, and diff‑level reasoning—to auto‑detect regressions and fix‑forward when possible.

You stay in control the whole way: review phase plans, trace changes back to requirements, compare agent outputs when needed, and merge with confidence via a PR‑style experience. Plan. Orchestrate. Verify. Ship. We’re just getting started.

Capabilities at a Glance

• Phases (Task Decomposition): Deterministic planning that converts objectives into ordered, verifiable steps.
• Detailed implementation plans: Translate each phase into a thorough plan that can then be handed off to an agent for execution.
• Agent Handoff: Route each phase to your preferred coding agent(s).
• Verification: Every phase includes a verification step. Changes are checked against your intent, and regressions are caught early.
• Human‑in‑the‑Loop: PR‑style review, inline rationale for changes, and one‑click accept/revise.
• MCPs: Integrate with central systems of record like Linear/Jira/GitHub/Sentry Issues for plans rooted in your organization’s data.
• Rules: Capture and standardize how you plan inside your org.

How It Works

1. Capture Intent from a ticket or prompt; extract constraints and acceptance criteria.
2. Plan as Phases with explicit inputs/outputs, owners (agent vs. human), and success checks.
3. Dispatch to Agents per phase; select the best model/tooling for the job.
4. Verify Outputs analysis, and spec checks; auto‑iterate and fix if needed.
5. Move to the next Phase as you build entire features and apps all inside Traycer

Why Traycer?

Traycer bridges the gap between planning and generation, making agent‑written code reliable, auditable, and aligned with what you asked for. Teams get faster delivery without sacrificing quality: less thrash, fewer regressions, and a repeatable path from intent → plan → changes that ship.