AI orchestration that holds up in production, not just the demo.

AI orchestration is the coordination layer that manages multiple AI models, tools, and data sources working together in a pipeline or agent workflow. A single LLM call handles a single task. AI orchestration handles: calling a retrieval system before the LLM, routing between models based on task type, managing state across multi-step agent workflows, handling tool use results and errors, and retrying failed steps. Orchestration is what turns a demo into a production AI system.

A simple API call is sufficient when: your task is single-step, inputs fit in the context window, you need one model's output, and failure handling is not critical. AI orchestration is needed when: your workflow requires multiple steps (retrieve, analyse, generate, validate), you need to route between models based on task complexity or cost, your agent uses tools that produce results it needs to reason about, you need to maintain state across a conversation or workflow, or failures in one step need graceful fallback rather than a full error.

LangGraph is an open-source orchestration framework for building stateful AI agent workflows as directed graphs. Each node in the graph is an AI step or tool call; edges define the routing logic. LangGraph handles state management, cycles (when an agent needs to loop or retry), and parallel execution. We use LangGraph for complex agent workflows with many states, conditional branching, and human-in-the-loop requirements. For simpler pipelines, custom orchestration without a framework is often cleaner and more maintainable.

Every orchestration step can fail: API rate limits, model unavailability, tool execution errors, and unexpected model outputs. Production orchestration requires: retry logic with exponential backoff for transient failures, fallback paths when a primary model fails, circuit breakers to stop cascading failures, dead letter queues for failed workflow runs that need human review, and alerting when failure rates exceed thresholds. We design failure handling as part of the orchestration architecture, not as an afterthought.

Multi-step AI workflows accumulate context that can exceed model context windows. Management strategies: summarisation (compress earlier workflow steps into summaries), selective context (include only the most relevant prior steps based on the current task), external memory (store workflow state in a database rather than the context window), and context chunking (process large inputs in segments). The right strategy depends on your workflow structure and the information dependencies between steps.

A framework earns its place when it solves a non-trivial problem: state persistence, loops, retries, tool coordination. For a single-turn agent, frameworks add latency, update churn, and debugging overhead a short custom loop avoids. The test is whether you are fighting the framework: if its abstractions do not match your control flow, it costs more time than it saves. Start managed for the MVP, migrate to custom for production: custom gives you full audit trails and control over where data flows.

Track cost per completed outcome, not per model call. The cheapest model is not the cheapest workflow if it retries twice or needs more validation. Demand token-aware budgets per tenant, semantic caching, smart model routing, and prompt compression. And set a per-task ceiling so one runaway loop cannot burn a week's inference budget in an afternoon.

Ask what happens when a step fails mid-workflow: can it retry safely, resume from a checkpoint, or route to a human? Look for an observability layer that shows every step, its cost, and its outcome, not a black box. Ask how the vendor prevents a runaway loop from becoming a runaway bill. And test with your hardest real documents: the demo works, the first real document is where orchestration earns its keep.

Pricing is set after we scope your workflow, integration points, and failure-handling requirements. You get a fixed price in writing before development starts, and a scope change is a priced change request you approve first. Most builds start with a single workflow before routing expands to the rest.