AI Orchestration: Patterns, Platforms, and Shared State
AI orchestration coordinates multiple AI agents or model calls to complete a task neither could finish alone. Here is how the main patterns work, what platforms provide, and what you need to manage yourself.
Updated 4 min read
AI orchestration means running more than one AI model call in a coordinated sequence so that a larger task gets done than any single call could handle. The orchestrator decides which model to call, what to pass it and what to do with the result. Understanding the main patterns helps you choose a platform and design your system so state does not get lost between steps.
An orchestrator manages execution, but it does not hold your project's decisions or handoff context. Tools like fenbs sit outside the orchestration layer and supply that shared record to every agent that needs it.
The main orchestration patterns
Three patterns cover most practical orchestration use cases. You will often combine them in one system.
- Sequential pipeline: calls run in order, each taking the previous output as input. Straightforward to debug because you can inspect the result of each step.
- Parallel fan-out and gather: the orchestrator calls multiple models or tools at once, then combines their results. Useful when tasks are independent and speed matters.
- Routing: the orchestrator reads the request and decides which specialist model or agent handles it. Useful when different inputs require different expertise.
A fourth pattern, agent loops, lets a model call tools, inspect results and decide the next tool call until the task is complete. This is less predictable than the first three and needs clear stopping conditions and a budget for the number of steps.
Execution state: what the orchestrator holds
Execution state is the data that passes between steps: the model's output, intermediate results and tool call results. Most orchestration platforms hold this in memory for the duration of a run. When the run ends, that state is gone unless you persist it somewhere.
Persisting state is your responsibility. Decide which outputs are worth keeping, in what form, and where. The platform provides the pipes; you provide the storage. This distinction matters when a run fails partway through: without persisted state, you cannot resume from where it stopped.
What orchestration platforms provide
Orchestration platforms differ in how much of the plumbing they handle for you. At minimum, they provide a way to define a graph of steps and route data between them. More capable platforms add retry logic, parallel execution, tool registries and observability.
| Capability | Why it matters | What you still provide |
|---|---|---|
| Step graph definition | Describes which calls run in which order and what data flows between them. | The design: which steps are needed and what each one should do. |
| Tool registry | Lists the tools the agent can call, with their schemas. | The tools themselves and the logic inside them. |
| Retry and error handling | Retries failed steps, routes errors to fallback paths. | Deciding which errors are retryable and what a failure means for the task. |
| Observability | Traces each step's input and output for debugging. | Reviewing the traces and acting on what they show. |
| Execution state | Holds intermediate results during a run. | Deciding what to persist after the run ends and where. |
Keep project decisions outside the orchestration layer
An orchestration platform runs the task; it does not hold the project context that the next run needs. In fenbs, keep the decisions about how the orchestration should work, Where we left off between long runs, and any lessons from failed attempts. Every connected AI reads those before it acts.
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Shared state across agents
When multiple agents work in parallel, they need a way to share state without conflicting. The simplest approach is to design agents that do not need to write to shared state: each agent gets its input, produces its output, and the orchestrator combines them. When agents must write shared state, use a database with locking or a message queue, not a shared object in memory.
Project decisions, rules and the current task list are a different kind of shared state: they change infrequently and need to be readable by every agent. Keeping them in a record outside the orchestration layer, such as fenbs, means any agent can read them without the orchestrator needing to pass them explicitly.
When to use orchestration and when not to
Orchestration adds complexity. A single well-prompted model call that handles the whole task is better than an orchestrated pipeline that coordinates three model calls to do the same thing. Start with the simplest approach that produces the result you need.
Add orchestration when a task is genuinely too large for one context window, when different parts require different specialist models, or when you need parallelism for speed. Do not add it because it feels more powerful. The cost is in design, debugging and the additional failure modes each step introduces.
Documenting your orchestration design
Orchestration systems tend to grow in ways that are hard to retrace later. The decision to add a routing step, the reason a particular model handles one category of input, and the conditions that determine how many agent loop iterations are allowed are all things that are obvious when you build them and opaque six months later.
How to do this with fenbs
- Create a free account and add your orchestration project.
- Record the key decisions about your orchestration design in Decisions and rules: which patterns you use, why, and what the stopping conditions are.
- After each long run, use Where we left off to record where the run reached, what succeeded and what the next step is.
- Add a Lessons learned entry when a run fails for a non-obvious reason, so future sessions check for that failure mode.
- Connect your AI over MCP from Integrations and ask it to read the project context, including rules and recent lessons, before designing the next change.