Orchestration is the runtime layer that coordinates multi-step agent execution. The LLM thinks; the orchestration decides which step runs next, retries when something fails, evaluates whether the goal is met, and escalates when it cannot recover. Most agent reliability work in 2026 lives in the orchestration layer, not in model choice. The shape of the layer is what separates a working prototype from a production-grade autonomous system.

This post defines orchestration, walks through the planner-executor-evaluator pattern that dominates production design, covers the conditions where multi-agent orchestration helps, surveys the framework landscape, and explains why orchestration is the largest reliability lever buyers can ask about. The framing draws on Anthropic's engineering guidance (retrieved 2026-05-07) and the AgentBench cross-environment benchmark (Liu et al., 2023).

What orchestration actually is

Orchestration sits above the LLM and below the application. It receives a goal, produces a plan (or asks the LLM to produce one), executes steps via tool calls, evaluates each step's output, decides whether to continue, retries on transient failures, replans on structural failures, and escalates when it cannot recover. The orchestration layer is the thing that turns "an LLM that can call tools" into "an agent that can finish a task."

The vocabulary is settled. A "step" is a unit of work: one tool call plus its surrounding reasoning. A "trace" is the sequence of steps for a single task. A "planner" produces the step sequence. An "executor" runs steps. An "evaluator" checks results. A "router" picks among reasoning patterns or sub-agents. None of these are exotic; they are the same primitives any workflow runtime uses, with one difference: the next step is decided by an LLM, not a static graph.

The planner-executor-evaluator pattern

The planner-executor-evaluator pattern is the dominant production design in 2026. Three components, distinct responsibilities, separate prompts (and sometimes separate models). The planner reads the goal and produces a step sequence. The executor runs each step, usually via ReAct (reasoning explained). The evaluator checks whether the step's output is correct against the goal.

  1. Planner. Decomposes the goal into steps. Output: an ordered list of intended steps with success criteria for each.
  2. Executor. Runs each step. Calls tools (tool use), reads results, produces step output. Operates in ReAct mode by default.
  3. Evaluator. Checks whether the step's output matches its success criteria. On failure: replan, retry, or escalate.

Splitting these three is what makes the system debuggable. A failed task with one log line ("agent gave up") is opaque. A failed task with separate planner, executor, and evaluator logs lets the operator see whether planning was wrong, execution was wrong, or evaluation was wrong. The 80-test methodology stresses each stage independently because each stage can fail differently.

The pattern also enables outcome-described tasks. The user provides the desired end-state; the planner produces the path. Without explicit planning, outcome-described tasks degenerate into "the LLM decides everything," which is the failure mode stop-after-one-task describes. The product framing in describe outcome, not workflow assumes a planner-executor-evaluator orchestration underneath.

Planner-executor-evaluator pattern Planner step sequence Executor ReAct + tools Evaluator success check step result retry / replan replan from scratch on hard failure Goal in Done out Source: Adapted from Anthropic engineering, AgentBench (Liu et al. 2023), and standard agent runtime patterns.
Solid arrows are the success path. Dashed arrows are the recovery paths. Most production failures live on the recovery paths.

When multi-agent orchestration helps

Multi-agent orchestration extends the pattern by spawning sub-agents for parallel subgoals. A research task that needs simultaneous searches in three different domains is a natural fit; one sub-agent per domain, results aggregated. A coding task that requires touching three independent files might also benefit. The condition is parallelisable subgoals; if the subgoals are sequential, multi-agent adds coordination overhead with no speedup.

The cost of multi-agent is non-trivial. Coordination requires a meta-agent or a shared state store. Each additional agent multiplies inference spend. Anthropic engineering guidance notes that orchestration complexity grows non-linearly with agent count, and many "multi-agent" wins evaporate when the comparison is fair (one agent with parallel tool calls against many agents). The pragmatic 2026 default: single-agent with parallel tool calls; reach for multi-agent only when subgoals are genuinely independent and the meta-agent overhead is justified.

For most business tasks (lead follow-up, status reporting, research aggregation), single-agent with a good tool catalogue and a planner-executor-evaluator orchestration is correct. Multi-agent shines in research, code, and creative tasks where parallel exploration is cheap to merge. The single-agent vs multi-agent post in this cluster covers the cost trade-offs in detail.

The frameworks landscape

Several frameworks implement the orchestration primitives. LangChain and its newer graph-based companion LangGraph are the most widely adopted in 2026, with strong observability tooling. LlamaIndex offers an AgentRunner with similar primitives. Autogen and CrewAI focus on multi-agent setups. OpenAI's Assistants API and Anthropic's Model Context Protocol (MCP) provide vendor-aligned alternatives. None of these implement different capabilities; they implement the same primitives with different ergonomics, language support, and observability hooks.

The buyer-side question is rarely "which framework"; it is "what does the orchestration look like in production logs". A framework with terrible observability is worse than no framework with good observability. The most predictive question for assessing an agent platform: ask to see a redacted production trace for a typical task. If the trace shows clearly-separated planner, executor, and evaluator outputs with retry events labelled, the orchestration is real. If it shows a wall of LLM tokens with no structure, the orchestration is hopeful.

Why orchestration dominates reliability

Most agent failures happen between steps. The LLM produces a reasonable thought; the orchestration layer drops state, retries the wrong step, skips the evaluator, or fails to surface a recoverable error. GAIA and SWE-bench both show steep pass-rate drops with multi-step task length, and the dominant cause is orchestration-layer error compounding (Mialon et al. 2023; SWE-bench leaderboard, retrieved 2026-05-07).

The implication for buyers is direct: model choice is rarely the binding constraint. Orchestration design and reliability discipline are. A vendor that emphasises model choice over orchestration is selling the wrong story. The reliability discipline is documented in 80-test methodology; the framework choice is downstream of the discipline, not upstream.

For Gravity, the orchestration is the central piece of the product; the model is one component among several. The framing rule from three startups, three shutdowns applies: build only what is at least three times better than the alternative. Orchestration with reliability discipline is one of the few areas where the gap is large enough to clear that bar.

Frequently asked questions

What is AI agent orchestration?

Orchestration is the runtime layer that coordinates multi-step agent execution: planning the steps, executing them through tool calls, evaluating results, retrying or replanning when steps fail, and deciding when to escalate. Orchestration sits above the LLM and below the application; it is where most agent reliability work happens in 2026.

What is the planner-executor-evaluator pattern?

A common multi-component orchestration pattern: a planner produces a sequence of steps from the goal, an executor runs each step (often via ReAct), and an evaluator checks whether the result matches the goal. The pattern works well when the task has clear completion criteria and the planner can produce decomposable steps. It is a default for outcome-described agents.

When should an AI agent use multiple sub-agents?

Multi-agent helps when subgoals genuinely parallelise (independent research streams, parallel tool calls across separate domains) and when coordination overhead is justified by the parallel speedup. For most business tasks, single-agent with multiple tools wins on cost and reliability. Multi-agent is an optimisation, not a default; complexity scales non-linearly with agent count.

What orchestration frameworks exist for AI agents?

LangChain and LangGraph are the most widely adopted. LlamaIndex offers AgentRunner. Autogen and CrewAI focus on multi-agent. OpenAI offers the Assistants API; Anthropic offers MCP for tool servers. Each implements similar primitives differently. The choice usually hinges on language ecosystem and observability rather than capability ceilings.

Why is orchestration the largest reliability lever?

Because most agent failures happen between steps, not within them. The LLM gives a reasonable answer; the orchestration layer drops state, retries the wrong step, or skips the evaluator. Anthropic engineering guidance and the GAIA benchmark both point to multi-step coordination as the dominant reliability axis. The 80-test methodology weights orchestration-driven failures heavily.

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