Orchestration coordinates steps, context, tool calls, and handoffs in an AI workflow. Agent Framework is one example of software for organizing agents and workflows, including supported tool integrations.
Make the execution path explicit.
Orchestration decides which step runs next, what context it receives, and how results move between components. It can combine deterministic application logic with model-based decisions. This is different from asking one model to produce a long answer: the workflow may interact with several systems and maintain state across operations.
State can include task progress, retrieved references, pending approvals, and tool results. Keep only what the next steps need, protect sensitive values, and define how state is stored or discarded when the task completes or fails.
Control handoffs between components.
Each step needs an input contract, an expected output, and an accountable execution identity. A downstream step should validate what it receives instead of assuming an earlier model produced correct or authorized content. Human approval should apply to a specific proposed action rather than an open-ended chain.
Multiple agents can divide specialized work, but each handoff adds coordination and failure cases. Use additional agents when they provide a clear functional boundary, not simply because the framework makes them easy to create.
Example: reviewing a document.
A workflow could retrieve an approved document, extract claims, compare them with a policy source, and produce a review draft. Retrieval and permission checks can be deterministic. Interpretation may use a model. Publication can remain a separate, explicitly approved operation.
If policy retrieval fails, the workflow should not quietly substitute a general answer and report success. If publication was accepted before a timeout, a retry should not create a second copy. These decisions belong in workflow state and recovery logic.
Operate the workflow as software.
Define timeouts, bounded retries, cancellation, and escalation paths. Trace important steps with correlation information so operators can distinguish a model failure from a data or tool failure. Evaluate changes to prompts, models, integrations, and execution logic together. A framework organizes the workflow, but testing, access controls, and operational ownership remain application responsibilities.
Further reading
For tool boundaries and failure testing, read AI Security. For approval and change decisions, read AI Governance.