Agent Orchestration

What is Agent Orchestration?

Agent orchestration is the coordination of LLM-driven agents, their tools, and shared state so that a multi-step task is carried out reliably. It answers questions like how a task is broken into subtasks, which agent or tool handles each step, how steps run in sequence or parallel, how intermediate results pass between steps, and when the process should stop or ask a human.

Anthropic's guidance in "Building Effective Agents" draws a useful line between two designs. Workflows are systems where LLMs and tools are coordinated through predefined code paths. Agents are systems where the LLM dynamically directs its own process and tool use. Orchestration spans both: a workflow orchestrates steps through code you wrote, while an agent orchestrates its own steps at runtime within a loop you provide.

The practical point is that orchestration is an engineering concern, not just a prompting one. The reliability of a multi-step system depends on how state is managed, how failures are handled, how tools are described, and how control flows between components, as much as on the quality of any single model call.

• Coordination of agents, tools, and shared state across a multi-step task.
• Covers decomposition, routing, sequencing, parallelism, result-passing, and stopping.
• Spans predefined workflows (code-driven paths) and autonomous agent loops.
• Anthropic distinguishes workflows (predefined paths) from agents (self-directed).
• Reliability depends on state, failure handling, and control flow, not prompts alone.

Common orchestration patterns

Anthropic catalogs several composable workflow patterns. Prompt chaining decomposes a task into a fixed sequence of steps, where each LLM call works on the previous output, often with programmatic checks between steps. Routing classifies an input and sends it to a specialized handler, which keeps each prompt focused. Parallelization runs multiple LLM calls at once, either by sectioning independent subtasks or by voting, running the same task several times for higher confidence.

Two patterns are more dynamic. The orchestrator-workers pattern uses a central LLM to break an unpredictable task into subtasks at runtime, delegate them to worker calls, and synthesize the results. The evaluator-optimizer pattern pairs a generator with an evaluator that critiques the output and feeds back revisions in a loop until quality is acceptable.

Beyond these workflows sits the autonomous agent: a model that plans, calls tools, observes results, and loops until a stopping condition or human checkpoint is reached. Anthropic's recurring advice is to start with the simplest pattern that works and add autonomy only when the added complexity clearly pays off.

• Prompt chaining: a fixed sequence of steps with checks between them.
• Routing: classify the input and send it to a specialized handler.
• Parallelization: sectioning independent subtasks or voting across repeated attempts.
• Orchestrator-workers: a central LLM decomposes, delegates, and synthesizes at runtime.
• Evaluator-optimizer: a generator paired with an evaluator that critiques and revises in a loop.

Coordinating state, tools, and multiple agents

State is what makes orchestration reliable. The system must track what has been done, what each step produced, and what remains, so later steps can use earlier results and so a failed step can be retried without corrupting the run. In multi-agent setups, agents also need a way to share context, whether through a shared scratchpad, message passing, or handoffs.

Tools are the agent's interface to the world, and Anthropic stresses designing the agent-computer interface as carefully as the prompts: clear tool names, documented parameters, and well-shaped return values so the model can use tools correctly. Standardized protocols help here. The Model Context Protocol (MCP) standardizes how an agent connects to tools and data sources, and the Agent2Agent (A2A) protocol, created by Google and contributed to the Linux Foundation, standardizes how independent agents discover and communicate with each other, using Agent Cards to advertise capabilities.

When tasks exceed what one agent can do well, orchestration becomes multi-agent: a lead agent plans and delegates to specialized sub-agents, then synthesizes their outputs. This adds power but also coordination cost, so it is justified mainly for tasks that genuinely decompose into independent, parallelizable parts.

• State tracks completed steps, intermediate results, and remaining work for retries and reuse.
• Agents share context through scratchpads, message passing, or explicit handoffs.
• Tool interfaces need clear names, documented params, and well-shaped returns.
• MCP standardizes agent-to-tool connections; A2A standardizes agent-to-agent communication.
• Multi-agent orchestration delegates to specialized sub-agents, with added coordination cost.

Code: a minimal orchestrator loop with tools

The example below shows the core of an autonomous agent loop: the model is given tools, and the orchestrator runs a loop that executes whatever tool the model requests, returns the result, and continues until the model produces a final answer instead of a tool call. This loop, together with state and a stopping condition, is what most agent orchestration is built on.

import anthropic

client = anthropic.Anthropic()

tools = [{
    "name": "search_docs",
    "description": "Search the knowledge base and return matching snippets.",
    "input_schema": {
        "type": "object",
        "properties": {"query": {"type": "string"}},
        "required": ["query"],
    },
}]

def run_tool(name, args):
    if name == "search_docs":
        return f"results for: {args['query']}"
    return "unknown tool"

messages = [{"role": "user", "content": "Find our refund policy and summarize it."}]

for _ in range(10):  # stopping condition: max steps
    resp = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=1024,
        tools=tools,
        messages=messages,
    )
    messages.append({"role": "assistant", "content": resp.content})

    if resp.stop_reason != "tool_use":
        print(resp.content[-1].text)  # final answer
        break

    tool_results = []
    for block in resp.content:
        if block.type == "tool_use":
            output = run_tool(block.name, block.input)
            tool_results.append({
                "type": "tool_result",
                "tool_use_id": block.id,
                "content": output,
            })
    messages.append({"role": "user", "content": tool_results})

Best practices and pitfalls

Start simple. Many tasks that look like they need a fleet of agents are better served by a single well-prompted call or a short, predefined workflow, which is cheaper, faster, and easier to debug. Add autonomy and additional agents only when the task demands open-ended decision-making that fixed code paths cannot express.

Prioritize transparency and control. Make the agent's plan and tool calls observable, set explicit stopping conditions and step limits to prevent runaway loops, and insert human checkpoints for high-stakes actions. Because agents act in loops, small per-step errors can compound, so validating results between steps matters.

Manage context deliberately. Long-running agents accumulate history that can hit context limits and suffer from issues like lost-in-the-middle, so summarize or prune state and retrieve only what each step needs. A dedicated memory layer such as MemX can hold durable, user-scoped context across runs, private by architecture with per-user isolation, so the orchestrator passes the model curated context instead of an unbounded transcript.

• Prefer the simplest design: single call or short workflow before multi-agent autonomy.
• Make plans and tool calls observable; set step limits and stopping conditions.
• Add human checkpoints for high-stakes actions, since per-step errors compound.
• Validate intermediate results between steps to catch errors early.
• Manage context with summarization, pruning, and a durable memory layer.