AI orchestration

01why it matters for a business

Once a company has more than one AI use case, the hard problems stop being about any single model. They are about the glue: sending simple requests to a cheap, fast model and hard ones to a stronger one, fetching the right records before a model answers, retrying a failed call, pausing for approval, and recording what happened. That glue is orchestration, and it decides cost, reliability, and auditability far more than model choice does.

Good orchestration also protects you from lock-in. If routing and context handling live in your own layer, swapping one model provider for another is a configuration change rather than a rebuild. It is also where you enforce policy: which data may go to which model, spend limits per task, and who can approve what.

02what it looks like in practice

A regional insurer handles first notice of loss through a portal. The orchestration layer receives each claim, runs a small classifier to tag the claim type, calls a retrieval step to pull the policy, sends the claim and policy to a larger model to draft a coverage summary, and routes anything above a dollar threshold to an adjuster queue. If the policy lookup fails, it retries, then flags the claim instead of letting the model guess. Every step is logged with inputs, outputs, model version, and cost.

03common mistakes

• Hard-coding one vendor's model into every step. Put routing behind your own interface so models can change.
• No failure handling. Decide what each step does when a tool times out or returns nothing; never let the model fill the gap by guessing.
• Building orchestration separately for each use case, so every team reinvents logging, authentication, and cost tracking.
• Ignoring cost per task. Track spend per run, not just the monthly bill, so you can see which steps are expensive.

04related terms

• AI agentAn AI agent is software that uses a language model to pursue a goal by choosing its own next steps: it reads the situation, picks a tool or action, checks the result, and repeats until the task is done or it needs a person.
• Multi-agent systemA multi-agent system is a setup in which several AI agents, each with its own role, instructions, and tools, work together on a task.
• Agentic workflowAn agentic workflow is a business process in which an AI model handles some of the steps itself, deciding how to complete them, while the overall sequence, rules, and handoffs are defined in advance.
• Model Context Protocol (MCP)The Model Context Protocol (MCP) is an open standard, introduced by Anthropic in 2024, for connecting AI applications to external tools and data.
• Inference costInference cost is what it costs to run an AI model to produce outputs, as opposed to the cost of training it.
• Reasoning modelA reasoning model is a language model trained to work through a problem step by step before giving its final answer, spending extra computation on intermediate thinking.

05where insomnia club fits