AI stack
Coordinate models, data, agents, and tools
A scope map for separating model routing, data access, workflow control, and agent coordination.
AI orchestration is a stack
AI orchestration coordinates the components around an AI application: model access, context and data, tools, workflows or agents, policy, evaluation, and operations. Products use the term for different slices of that stack, so architecture decisions should begin with the control problem rather than a vendor category.
- Model layer: provider selection, fallback, budgets, and latency
- Context layer: retrieval, memory, data access, and provenance
- Action layer: tools, APIs, sandboxes, and permissions
- Control layer: workflows, agents, state, approval, and recovery
Tracing · evaluation · cost · recovery
Identity · policy · approval · retention
Workflows · agents · state · checkpoints
Tools · APIs · sandboxes · external systems
Retrieval · memory · provenance · source records
Providers · routing · structured output · fallback
Model routing is one control problem
Choosing a model by task, cost, latency, context size, or reliability is useful, but it is not the whole orchestration system. Keep provider-specific clients behind an interface, validate structured outputs, and define fallback behavior explicitly rather than retrying every failure with a larger model.
- Route by capability and risk, not only by token price
- Separate transient provider failures from invalid model output
- Record which model and configuration produced each decision
- Test fallbacks for behavior changes as well as availability
Context and data need their own boundary
Retrieval, conversation memory, workflow state, and authoritative business records serve different purposes. The orchestration layer should decide what data is available to each step, preserve provenance, and keep external systems as the source of truth rather than copying uncontrolled state into a prompt.
- Retrieve only context relevant to the current decision
- Track sources and versions used for consequential outputs
- Separate short-lived working memory from durable records
- Apply access control before information reaches the model
Tools turn output into action
Tools let models fetch data, call APIs, run code, or operate in a workspace. The runtime must validate schemas, authorize the caller, handle timeouts and errors, and isolate risky execution. Tool availability is a policy decision; it should not depend only on a prompt asking the model to be careful.
- Use small, well-described interfaces with explicit error behavior
- Separate read, propose, and execute permissions
- Require approval for irreversible or externally visible actions
- Capture tool inputs and outputs without leaking sensitive data
Workflow and agent control are distinct
Workflows define or constrain the path; agents dynamically choose more of it. A reliable system can combine sequential, parallel, loop, routing, manager, and handoff patterns. Agentic orchestration is the narrower problem of coordinating those model-driven actors inside the wider AI stack.
- Keep stable business rules and terminal states deterministic
- Use model decisions only where the path genuinely varies
- Persist state and checkpoints for long-running or interruptible work
- Make ownership explicit when control moves between agents
Policy and governance cross every layer
Identity, data access, model policy, tool permissions, approval requirements, retention, and audit logging cannot be bolted onto the final response. They must be evaluated at the relevant boundary and enforced even when a model recommends a different action.
- Tie every run and tool call to an authenticated principal
- Minimize data exposure and define trace retention deliberately
- Apply budgets and rate limits to both users and automated runs
- Fail closed when required policy context is unavailable
Observability must follow the whole run
AI orchestration needs end-to-end traces that connect model generations, retrieval, tools, handoffs, guardrails, approvals, retries, and final outcomes. OpenAI's Agents SDK, for example, models a workflow trace as related spans and warns that model and tool inputs can contain sensitive data.
- Use one run identifier across services and agent boundaries
- Record latency, cost, retries, errors, and terminal state
- Redact or disable sensitive trace payloads where required
- Connect operational traces to outcome evaluation
Evaluate platforms at the seams
The hardest failures occur between layers: stale context reaches a good model, a correct proposal receives excessive permission, or a completed tool call is retried after a timeout. Evaluate an orchestration platform by how well it exposes and controls these boundaries—not by the number of agent templates in a gallery.
- Portability of models, prompts, tools, and workflow state
- Recovery from partial failure and long-running interruption
- Policy enforcement independent of model cooperation
- Trace quality, cost visibility, testing, and evaluation support
One stack, several control problems
AI orchestration spans model selection, retrieval, tools, workflows, policies, evaluation, and operations. Agentic orchestration is one narrower layer within that system.
Use the framework above as a review sequencePrimary sources
Documentation reviewed for this guide
Claims about framework behavior are tied to current first-party documentation. The design recommendations are our synthesis of those documented capabilities.
- 01OpenAIOpenAI Agents SDK ↗
- 02
- 03LangChainLangGraph overview ↗
- 04MicrosoftMicrosoft Agent Framework overview ↗
- 05
- 06AnthropicBuilding effective agents ↗