What is AI agent orchestration?
AI agent orchestration is the code and logic that coordinates AI models, tools and agents towards a goal: it decides what runs next, gives each step the context it needs, and checks results before moving on. The orchestration can be fixed in code (a workflow) or partly left to a model that chooses its own next step (an agent). Most production systems mix the two and keep the riskiest decisions in code or with a person.
- Published
- 22 October 2026
- Sources checked
- 22 October 2026
- From
- Kramiva
01
What the term covers
A single model call answers one prompt. Anything more — several steps, several tools, several models — needs something to decide the order, carry information between steps, and know when to stop. That coordinating layer is the orchestration.
Anthropic draws the distinction most guides now use. Workflows are systems where models and tools are orchestrated through predefined code paths; agents are systems where the model dynamically directs its own process and tool use. Both count as orchestration — they differ in who decides the next step.
OpenAI's guide to building agents describes the same idea for systems with several agents: a manager pattern, where one central model calls other agents as tools, and a decentralised pattern, where agents hand a task to one another.
02
How it works
Whatever the framework, an orchestrated run has the same parts. Frameworks differ mainly in how much of this they write for you and how much they let you see.
- A trigger or goal: a user request, an incoming email, a scheduled job
- A plan: fixed in code, or produced by a model at run time
- Steps dispatched to models, tools or other agents, in sequence or in parallel
- State carried between steps, so each one sees what it needs and nothing it should not
- Checks on the output: code assertions, a second model acting as grader, or a person
- A stopping condition: the task is done, a limit is reached, or a person must decide
03
An illustrative example
Take a customer-support inbox. A routing step classifies each message. For a delivery question, a model drafts a reply from the company's own policy documents. For a refund, ordinary code checks the amount: small refunds go out with the drafted reply, larger ones wait for a person to approve.
That last rule follows OpenAI's guidance, which lists large refunds and payments among the high-risk actions that should trigger human intervention. The model does the reading and writing; the code and the person own the irreversible decision.
04
Trade-offs
More autonomy buys flexibility on open-ended tasks and costs predictability, latency and money. Anthropic's advice is to find the simplest solution that works and add complexity only when it demonstrably improves results.
The cost of getting this wrong is visible in industry forecasts. In June 2025 Gartner predicted that over 40% of agentic AI projects will be cancelled by the end of 2027, citing escalating costs, unclear business value and inadequate risk controls.
Where Kramiva fits
Kramiva's AI automation service builds with evaluation, guardrails and production monitoring, and anything irreversible — sending, paying, deleting, publishing — requires a person's approval. Feasibility work can honestly end in a recommendation not to build.
Sources
- Building effective agents — Anthropic, 19 December 2024
- A practical guide to building agents — OpenAI
- Gartner predicts over 40% of agentic AI projects will be canceled by end of 2027 — Gartner, 25 June 2025
Accessed 22 October 2026. Kramiva is not affiliated with these publishers.
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