AI coding agent orchestration is the practice of coordinating several AI coding agents, such as Claude Code and Codex, on one software goal. An orchestration layer splits the goal into tasks, assigns each task to a suitable agent, passes the relevant context from one agent to the next, holds changes for human review, and keeps the lessons worth reusing. The agents still write the code; orchestration manages the work between them.
Why teams need it
Many developers now use more than one coding agent. One agent is strong at investigating an unfamiliar codebase, another is preferred for a certain kind of implementation, and a third is a useful second opinion in review. Using them together is where the friction starts:
- Every agent starts cold. Each session needs the goal, the constraints and the relevant files explained again.
- Handoffs are manual. You copy findings from one agent’s output into another agent’s prompt and hope nothing important is lost.
- Changes collide. Two agents editing the same working tree can overwrite each other’s work.
- Lessons disappear. A fix that took an hour to reason through is forgotten by the next session, so the same mistake can return.
The developer ends up as the coordinator, which is exactly the work the agents were meant to take off their plate.
The four jobs of an orchestration layer
1. Plan and route
The goal is broken into tasks small enough for one agent to finish, and each task is matched to an agent or model based on what the task needs and the constraints you set. A good plan keeps those constraints attached to every task, so “the invitation must stay valid for seven days” is not lost on step three.
2. Carry context across handoffs
When one agent finishes, the next receives what it needs: the original goal, the finding or patch, the files involved and what has already been tried. This is the difference between a relay and a restart.
3. Gate changes on review
Agents can be wrong with confidence. Orchestration puts a review step between the work and your main branch: a person, often helped by a reviewing agent, checks the result against the goal before anything is merged.
4. Remember what worked
Reviewed fixes and project decisions are stored as lessons and supplied as context on related tasks later, whichever agent picks them up. This is context, not model training: the underlying models do not change.
Orchestration is not the same as…
- A model router. A router picks which model answers a prompt. Orchestration manages multi-step work across agents, including handoffs, review and memory.
- Subagents inside one tool. Some agents can split work into subagents of their own. Orchestration works across different agents and vendors, so you are not tied to one tool.
- A CI pipeline. CI runs fixed steps on code that already exists. Orchestration plans and coordinates the work that produces the code.
What to look for in an orchestration tool
- It works with the agents and subscriptions you already have, rather than replacing them.
- Your code and context stay in your own environment.
- Nothing reaches your repository’s main line without your approval.
- You can see the plan, every handoff and the evidence behind each result.
- Memory is reviewed by you, scoped to a project and traceable to its source.
Where COLLASTRA fits
COLLASTRA is a coordination layer for AI coding agents built around those four jobs. You connect GitHub and describe a task. COLLASTRA plans it into subtasks, runs Claude Code and Codex agents on your repository, and shows you the diff to approve before it opens a pull request. Lessons you approve are kept in OctaMem shared memory for the next agent. See how it works or the supported agents.