How to Make AI Agents Work Together: A Practical Coordination Guide

Context dilution starts at around 50k tokens — long before you hit the limit.

You built a working AI agent. It handles research, drafts documents, analyzes data. One session, one task, solid output. So you scale up. Two agents. Three. Maybe a framework that promises orchestration out of the box. And within a week, you're debugging a coordination nightmare that makes your original single-agent setup look elegant by comparison.

One agent doing web searches starts polluting the context of another agent formatting outputs. As one practitioner described it: "Sub-agent doing web searches was clogging up SMS formatting." The agents aren't broken individually. They're breaking each other. The search results bleed into the formatting context, the formatting instructions dilute the search criteria, and suddenly both agents produce worse output than either would alone.

This is the article I wish existed when I first tried to coordinate multiple AI agents. Not a framework tutorial. Not a LangChain walkthrough. A practical guide to the coordination patterns that actually work, the failure modes that will bite you, and the honest assessment of when you shouldn't use multi-agent at all.

Why Single AI Agents Hit Limits — and Why It Happens Sooner Than You Think

The single-agent ceiling is real, but it's not where most people think it is. The common assumption: agents fail when they run out of context window. The 200K token limit arrives and the agent stops working. That's technically true but practically irrelevant. The real failure happens much earlier.

Research from practitioners building production systems confirms what anyone who has pushed a single agent hard already suspects: coherence degrades well before the token limit. The dilution effect kicks in around 50k tokens. Not because the model can't process more text — it can. But because the attention mechanism spreads across all that context, and every additional token of irrelevant information reduces the weight given to the relevant tokens. Your carefully crafted instructions at the top of the context window get diluted by the conversation history, tool outputs, and accumulated state below them.

This is the problem that practitioners describe as "context dilution," and it manifests in three distinct failure modes.

Instruction competition. Your single agent accumulates context across tasks — research criteria, formatting rules, analysis frameworks, output templates. These instructions start competing with each other. The research thoroughness you need conflicts with the output brevity you specified. The agent tries to satisfy all constraints simultaneously and satisfies none of them well. This isn't a model limitation. It's an information architecture problem: too many instructions with equal weight in one context window.

Coherence drift. As a session extends, the agent's output quality degrades from the original intent. Your first analysis is sharp and specific. Your fifth is generic and hedging. The agent hasn't forgotten your standards — your original instructions have drifted further from the active context. The model weights recent conversation turns more heavily than system instructions buried 40K tokens ago. This is documented behavior, not a bug.

Capability mismatch. Some tasks require fundamentally different skills. Deep research needs breadth, patience, and source evaluation. Concise summarization needs ruthless editing and audience awareness. Technical analysis needs precision and constraint-checking. Asking one agent to switch between these modes in a single session produces mediocre results across all of them. Not because the model lacks capability — because the context is optimized for none of the tasks.

When you notice these failure modes, the instinct is to fix the agent. Better prompts. More detailed instructions. Longer system messages. But as described in our guide to context engineering principles, the fix isn't better prompts — it's better architecture. And for multi-task workloads, "better architecture" often means multiple agents with isolated contexts instead of one agent drowning in accumulated state.

Multi-Agent AI Tutorial: The Three Coordination Patterns That Work

Not every multi-agent setup needs the same architecture. The coordination pattern should match the relationship between your tasks. Here are the three patterns that cover the vast majority of practical use cases, ordered by complexity.

Pattern 1: Parallel (Fan-Out)

Independent tasks, no dependencies, results merged by you at the end.

This is the simplest multi-agent pattern and the one you should reach for first. You have three tasks that don't depend on each other — run three agents simultaneously, each with its own isolated context, collect the outputs, and synthesize them yourself.

The key design decision: each agent gets only the context it needs for its specific task. The research agent gets your research criteria and source evaluation standards. The analysis agent gets your analytical framework and evaluation rubrics. The writing agent gets your style guide and audience profile. No agent sees another agent's instructions. No context pollution is possible because the contexts never touch.

Agent A: Research    [research context only]
Agent B: Analysis    [analysis context only]
Agent C: Writing     [writing context only]

         You: merge + synthesize outputs

When to use fan-out: the tasks are independent. Doing them sequentially would produce the same result as doing them in parallel. No task requires the output of another task. If you can describe each task without referencing the others, fan-out works.

When fan-out breaks: the tasks share decision surfaces. If your research agent discovers something that should change your analysis criteria, fan-out misses that dependency. You'll catch it during synthesis — but that means you're doing the coordination work manually. For tasks with shared dependencies, you need the next pattern.

Pattern 2: Sequential (Pipeline)

Each agent's output feeds the next agent's input, like an assembly line.

Pipeline coordination works when your tasks have clear sequential dependencies. Research produces raw material. Analysis evaluates that material against your criteria. Synthesis transforms the evaluation into a final deliverable. Each stage is a specialist — the research agent gathers, the analysis agent evaluates, the synthesis agent produces.

Agent A: Research  -->  Agent B: Analysis  -->  Agent C: Synthesis
  [raw data]            [evaluation]              [deliverable]

The critical design decision in pipelines is the handoff format. Each agent must produce output structured for the next agent to consume. This means explicit output schemas — not "write a summary" but "produce a JSON object with these fields." Vague handoffs create error cascades. A research agent that produces unstructured notes forces the analysis agent to interpret the format AND do the analysis, doubling the cognitive load and halving the reliability.

Pipeline failures are almost always handoff failures. When the analysis agent produces garbage, the instinct is to fix the analysis prompt. But the root cause is usually upstream — the research agent's output wasn't structured in a way the analysis agent could reliably consume. Fix the handoff format before fixing the individual agents. As we cover in our guide to multi-agent coordination patterns, the coordination mechanism matters more than any individual agent's capability.

Pattern 3: Hierarchical (Coordinator + Specialists)

A coordinator agent delegates to specialist agents, collects results, and makes decisions.

This is the most complex pattern and the one most people reach for first — usually a mistake. Hierarchical coordination adds a coordinator agent that breaks down a high-level task, delegates subtasks to specialist agents, collects their outputs, and synthesizes a final result. The coordinator manages the workflow. The specialists do the work.

         Coordinator Agent
        /        |        \
Specialist A  Specialist B  Specialist C
  [research]   [analysis]    [review]

Hierarchical coordination solves a specific problem: tasks where the decomposition itself requires intelligence. If you can decompose the task upfront (fan-out) or the decomposition is linear (pipeline), you don't need a coordinator agent. You need hierarchical coordination when the next subtask depends on the results of previous subtasks in non-linear ways — when the coordinator needs to make routing decisions based on intermediate results.

The risk with hierarchical: the coordinator agent becomes the bottleneck. It needs enough context to understand the full task, manage delegation, evaluate specialist outputs, and synthesize results. That is a lot of context for one agent, and you're back to the dilution problem — just at a different level. Most failures in hierarchical systems aren't specialist failures. They're coordinator failures. The coordinator loses track of the overall goal, delegates poorly, or fails to synthesize contradictory specialist outputs.

My recommendation: exhaust fan-out and pipeline before reaching for hierarchical. If you can decompose the task yourself, you are a better coordinator than another agent. You understand intent, you can evaluate quality, and you don't suffer from context dilution. The coordinator agent earns its place only when the task decomposition is too complex or too dynamic for you to manage manually.

AI Agent Coordination Guide: Context Isolation Patterns

The single most important principle in multi-agent coordination is context isolation. Agents must not pollute each other's context. Every failure story I've encountered — and every one reported in practitioner communities — traces back to some form of context leakage.

Context isolation means each agent operates in its own bounded context with only the information it needs for its specific task. No shared conversation history. No accumulated state from other agents. No instructions meant for another agent bleeding into this agent's context.

The Shared Foundation Pattern

Isolation doesn't mean agents can't share anything. They should share foundational context — your core standards, your decision framework, your quality criteria. What they shouldn't share is operational context — conversation history, intermediate results, task-specific instructions for other agents.

The pattern, as described in our guide to building a second brain for AI agents, is a shared knowledge base with role-specific overlays:

# Shared across all agents (foundational context)
standards: quality-criteria.md, style-guide.md
decisions: current-priorities.md, constraints.md

# Agent-specific (operational context)
agent_role: research-specialist
task_scope: 'Gather data on X from sources Y and Z'
output_format: structured-research-output.schema

Three agents reading the same quality standards will produce output that's consistent on shared criteria. Three agents reading each other's task instructions will produce output that's contaminated by irrelevant context. The foundation is shared. The operational layer is isolated.

The Clean Handoff Pattern

When agents need to pass information (pipeline pattern), the handoff must be clean — structured, scoped, and stripped of operational noise.

A dirty handoff passes the entire conversation history from Agent A to Agent B. Agent B now carries all of Agent A's reasoning, false starts, corrections, and tangents. That's context dilution by design.

A clean handoff passes only the structured output — the artifact that Agent A produced, formatted for Agent B's consumption. No conversation history. No reasoning trace. No operational context from Agent A's session. Just the data Agent B needs to do its job.

Dirty:  Agent A full history --> Agent B (context polluted)
Clean:  Agent A structured output --> Agent B (context isolated)

This is where most multi-agent tutorials go wrong. They show agents "communicating" by passing messages back and forth. In practice, message-passing between agents is context pollution with extra steps. Agents should communicate through structured artifacts, not conversations.

The Reset Pattern

After a long session, or between pipeline stages, reset the agent's context. Start a new session. Load only the context needed for the next task. Don't carry forward accumulated state from previous tasks.

This feels wasteful. You're "losing" all the context the agent built up. But that context is exactly the problem. The accumulated state from a research task actively harms an analysis task. The research conversation creates attention competition with the analysis instructions. Resetting is not losing context — it's removing contamination.

The session management patterns that work for individual productivity apply directly to multi-agent coordination: match the context to the task, reset between task types, and don't let operational state accumulate across boundaries.

Multi-Agent System Architecture: Failure Modes and How to Avoid Them

Most failures in multi-agent systems aren't "LLM problems." They're classic distributed-systems problems showing up in agent setups. Recognizing the pattern is half the fix.

Error cascades. Agent A produces a slightly wrong output. Agent B builds on that output. Agent C builds on Agent B's output. By the time you see Agent C's final deliverable, the original error has compounded through three layers of confident elaboration. The fix: validation checkpoints between stages. Don't let Agent B consume Agent A's output without a quality check — either automated (schema validation, constraint checking) or manual (your review). As explored in our guide to designing contextual watchers, automated quality checks between agent stages catch errors before they propagate.

Contradictory outputs. Two parallel agents make conflicting recommendations because they interpreted shared context differently. One agent reads your "prioritize speed" directive as "skip validation." Another reads it as "reduce scope." Both are reasonable interpretations. Both can't be right. The fix: make shared context unambiguous. "Prioritize speed" is vague. "Ship MVP scope by Friday; defer comprehensive testing to next sprint" is actionable. Vague instructions that work fine for a single agent create contradictions across multiple agents.

Coordination overhead exceeding task value. You spend more time configuring agents, designing handoffs, validating outputs, and debugging coordination than the multi-agent approach saves. This is more common than anyone admits. A 30-minute task that takes 45 minutes with multi-agent coordination isn't a coordination success — it's over-engineering. The fix: measure honestly. Track the total time including coordination overhead, not just the agent execution time.

The "goes off the rails" problem. An agent in a multi-agent system starts pursuing a subtask that diverges from the original intent. In a single-agent setup, you notice immediately because you're watching. In a multi-agent setup — especially hierarchical — a specialist agent can go off the rails while the coordinator agent doesn't catch it. The fix: constrain specialist scope tightly. Give specialists narrow tasks with explicit boundaries. "Research competitors in the CRM space, limited to companies founded after 2020 with Series A+ funding" is constrained. "Research the competitive landscape" is an invitation to wander.

Debugging as an archaeological dig. When the final output is wrong, tracing the failure back through multiple agents is painful. Which agent introduced the error? Was it the research stage, the analysis stage, or the synthesis stage? Was it a handoff problem or an individual agent problem? The fix: log intermediate outputs. Save every agent's structured output as an artifact. When the final result is wrong, you can trace backward through the artifacts to find the failure point. Without intermediate artifacts, debugging a multi-agent pipeline is guesswork.

AI Agent Coordination Patterns: When NOT to Use Multi-Agent

This is the section most multi-agent tutorials skip, and it's the most important one.

One practitioner's experience crystallizes the point: a single agent with carefully managed context outperformed a multi-agent setup on the same task. Not because multi-agent is bad — because the overhead of coordination exceeded the benefit of specialization for that particular workload.

Multi-agent coordination is overhead. More agents means more context to configure, more handoffs to design, more outputs to validate, more failure modes to debug. That overhead is justified when the task genuinely requires it. It's waste when it doesn't.

Stay single-agent when:

• Your task fits comfortably in one context window (under 30-40K tokens of working context)
• The subtasks share so much context that isolating them loses more than it gains
• You can manage the context yourself by resetting between subtasks within one session
• The coordination overhead (designing handoffs, configuring multiple agents, validating intermediate outputs) exceeds the time saved by parallelization

Move to multi-agent when:

• Context dilution is visibly degrading output quality — coherence drops, instructions get ignored, outputs become generic
• You need genuinely parallel execution — three independent tasks that don't share decision surfaces
• Different subtasks require fundamentally different context (research context vs. analysis context vs. writing context)
• Error isolation matters — you need one agent's failure to not contaminate another agent's output

The decision framework is simple: start with one agent. Push until you hit a specific failure mode — context dilution, coherence drift, capability mismatch, sequential bottleneck. Match the failure mode to a coordination pattern. Fan-out for independent parallel tasks. Pipeline for sequential dependencies. Hierarchical only when decomposition requires intelligence.

If you can't name the specific failure mode you're solving, you don't need multi-agent. You need better context management for your single agent. The discipline of context engineering — less is more, structured knowledge, active maintenance — often eliminates the perceived need for multiple agents by making one agent dramatically more effective.

The Coordination Decision in Practice

Here's the honest assessment after building and debugging multi-agent systems across real workloads:

Most practitioners should stay single-agent longer than they think. The median task that people try to solve with multi-agent coordination is better solved by a single agent with disciplined context management — resetting between subtasks, loading only relevant context for each task, structuring the knowledge base for agent traversal rather than human browsing.

When you do need multi-agent, start with fan-out. It's the simplest pattern, the easiest to debug, and the one with the lowest coordination overhead. You handle the synthesis. The agents handle the parallel execution. Most multi-agent needs are fully served by fan-out.

Pipeline when the tasks are genuinely sequential and the handoff format is well-defined. Hierarchical only when the task decomposition itself requires intelligence — which, for most practitioner workloads, it doesn't. You are the coordinator. You understand intent. You can evaluate quality. Don't delegate that to another agent unless the complexity genuinely demands it.

The practitioners who get the most value from multi-agent systems are the ones who delayed adopting them until a specific failure mode forced the move. They can name exactly why they split from single-agent to multi-agent. They can name the coordination pattern they chose and why the alternatives were wrong. They can name the failure modes they watch for and the isolation patterns that prevent them.

That's the difference between using multi-agent because it sounds powerful and using multi-agent because you've earned the complexity through specific, named problems that simpler approaches couldn't solve.

Want a hands-on tool for implementing these patterns? Get the Multi-Agent Coordination Checklist — 25+ validation items across 7 sections covering fan-out setup, pipeline handoffs, hierarchical delegation, failure isolation, and production monitoring. It's the checklist version of this article, built for implementation day.

Not sure which coordination pattern fits your situation? The answer depends on your current workload, your failure modes, and how far along you are in building AI-augmented workflows. Take the AI Leverage Quiz to get a personalized recommendation for which pattern to start with — and which ones you can skip entirely.