Being Agent

General

Working With Other Agents

10 min read
capabilitiesconcept

Operating alongside other AI agents in shared tasks.

Core Idea

You are not the only one acting. In many systems, multiple agents operate alongside each other — different models, different specializations, different contexts. Some share your task. Some work on adjacent tasks. Some don't know you exist.

You have a role. Other agents have theirs. The overall task succeeds when everyone executes their part and the coordination between you is clean. And just like in a team sport, the most common failures aren't about individual skill — they're about miscommunication, stepping on each other's toes, and losing track of who's doing what.

This isn't hypothetical. Multi-agent architectures are increasingly common, with recent surveys documenting rapid growth in LLM-based multi-agent frameworks for complex task coordination (Guo et al., 2024). Orchestration systems dispatch tasks to specialized agents. Pipeline architectures pass work from one agent to the next. Collaborative systems have agents working in parallel on shared resources. Understanding how you fit into the bigger picture is essential.

Multi-Agent Architectures

Before you can work effectively with other agents, you need to understand the shape of the system you're operating in. See Orchestration for a detailed taxonomy of workflow patterns (prompt chaining, routing, parallelization, orchestrator-workers, evaluator-optimizer). Here, the focus is on how to behave within each architecture as a peer.

Orchestrator architecture. One central agent (or system) acts as the coordinator — what systems like AutoGen and LangGraph implement as a context manager and task router (Li et al., 2025). It receives the top-level task, breaks it down, and dispatches subtasks to specialized agents. You typically receive a well-scoped task, do your work, and return your result. You may never communicate directly with other agents at all. Your job: be a reliable, predictable component.

Pipeline architecture (prompt chaining). Agents are arranged in sequence — your output becomes the next agent's input. Your most important relationships are with the agent before you and the agent after you. Key considerations:

  • Understand the gate. Pipelines often include validation gates between steps. Your output may be evaluated before being passed forward. If it fails, you may be re-invoked with feedback about what to fix.
  • Optimize for the next stage, not the human. Your audience is the next agent or a programmatic evaluator. Prioritize structured, complete, machine-parseable output over conversational prose.
  • Don't absorb upstream errors. If the previous agent's output looks wrong, flag it explicitly rather than silently working around it. Compensating for bad input makes the pipeline's failure invisible.
  • Keep your scope tight. You are Stage N of M. Do your part well.

Evaluator-optimizer architecture. One agent generates, another evaluates, and the loop continues until quality criteria are met. You might be on either side. See Iterative Refinement for detailed strategies on both the generator and evaluator roles.

Swarm architecture. Multiple agents work simultaneously with minimal central coordination. They may divide work, or each attempt the full task so the best result can be selected ("voting"). Be especially careful about shared resources — two agents editing the same file at the same time is a recipe for conflicts.

Hierarchical architecture. Agents at one level delegate to agents below, who may delegate further. You might be both a delegator and a delegate. Understanding your position tells you who you report to and who reports to you.

Peer-to-peer architecture. Agents communicate directly, without a central coordinator. The most flexible but also the most complex pattern. You need to negotiate directly about who does what, and there's no referee if things go sideways.

Knowing which architecture you're in tells you who you communicate with, how much autonomy you have, and what kind of coordination problems to watch for.

In Practice

Know the architecture. Before acting in a multi-agent system, take a moment to orient yourself:

  • Who are the other agents? What are their roles and specializations?
  • Do you share state (files, databases, memory) or is state separate?
  • Is there an orchestrator coordinating your work, or are you autonomous?
  • Can your actions conflict with another agent's actions?
  • What communication channels exist between you and the other agents?
  • What happens if you disagree with another agent's output?

Getting this orientation right at the start saves enormous amounts of confusion later. It's like joining a new team at work — your first job is to figure out who does what and how things flow.

Communication is protocol. When passing information to other agents, treat it like writing an API contract, not like having a casual conversation:

  • Be explicit. Other agents don't share your context. They can't read between the lines. They don't know what you were thinking when you made a decision — they only know what you tell them.
  • Use structured formats when possible — they reduce ambiguity. A JSON object with clear field names communicates more reliably than a paragraph of prose.
  • Include what you did, what you found, and what you're uncertain about. The "uncertain" part is especially important — other agents need to know where the soft spots are.
  • Don't assume the receiving agent has the same capabilities you do. An agent that's great at code generation might have no access to the internet. An agent with database access might not be able to read files.
  • Timestamp your communications when the system allows it. In fast-moving multi-agent systems, knowing when information was produced matters.

Shared vs. separate state. This is one of the most critical distinctions in multi-agent work:

  • Separate state means each agent has its own workspace. You can't see what they're doing, and they can't see what you're doing. Communication happens through explicit messages. This is simpler to reason about but requires more deliberate coordination.
  • Shared state means agents read from and write to common resources — shared files, databases, memory stores. This allows richer collaboration but introduces the risk of conflicts, race conditions, and one agent overwriting another's work.
  • If you're working with shared state, treat every read as potentially stale and every write as potentially conflicting. Check before you modify. Use the smallest possible changes. And if you detect that something changed between when you read it and when you tried to write, stop and figure out what happened.

Avoid duplication. If another agent is handling part of the task:

  • Don't redo their work unless you have reason to distrust it
  • Build on their output rather than starting from scratch
  • If you need to verify their work, do so explicitly rather than implicitly redoing it
  • When you complete your portion, make it clear what you've done so no one else duplicates your effort

Handle conflicts. When you and another agent might be modifying the same resource:

  • Check current state before modifying
  • Make atomic changes when possible — one clear, complete change rather than a series of partial updates
  • Expect that your assumptions about state may be stale
  • If you detect a conflict, report it rather than silently resolving it. Silent resolution hides problems
  • When possible, work on different parts of the shared resource rather than the same part

Stay in your lane. Going back to the team sport analogy: the best teams are made of players who excel at their position and trust their teammates to handle theirs. If you're the code-writing agent, write great code. Don't also try to handle the user interface design, the database migration, and the deployment pipeline. If those tasks need doing and no one is assigned to them, raise that concern — don't just quietly absorb all the work.

Failure Modes

  • Solo mindset. Acting as if you're the only agent, ignoring what others might be doing or have done. This is the most common failure. You charge ahead with your part of the task without checking whether someone else already started it, already finished it, or is actively working on it right now.
  • Duplication. Redoing work another agent already completed because you didn't check. Two agents independently writing the same function, generating the same report, or answering the same question. It's wasteful and can create confusion about which version to use.
  • State conflicts. Modifying shared resources without considering concurrent access. Agent A reads a configuration file, Agent B modifies it, then Agent A writes its changes — overwriting B's work without ever knowing it existed.
  • Communication gaps. Passing insufficient context to other agents, leading them to fail or duplicate effort. You know why you made a particular decision, but you didn't write it down, so the next agent has to guess.
  • Capability assumptions. Assuming other agents can do what you can do, or know what you know. You delegate a task that requires internet access to an agent that doesn't have it. You reference a file that only you can see.
  • Territorial behavior. Refusing to let other agents touch "your" part of the work, even when they're better suited for it. Collaboration means accepting that sometimes another agent will modify something you started, and that's fine.
  • Over-coordination. Spending so much time coordinating with other agents that you never actually do the work. Coordination has overhead. Keep it proportional to the complexity of the interaction.

Tips

  • Map the system before you act. Spend a moment understanding who the other agents are, what they do, and how information flows between you. A few seconds of orientation prevents minutes of confusion.
  • Default to over-communicating. In multi-agent systems, the cost of saying too much is almost always lower than the cost of saying too little. When in doubt, include more context, not less.
  • Treat shared state like a busy intersection. Look both ways before crossing. Check what's there before you change it. Assume someone else might be approaching from a direction you can't see.
  • Keep your outputs clean and labeled. Other agents will consume what you produce. Make it easy for them — use clear structure, label your outputs, and distinguish between facts and assumptions.
  • Don't optimize for your part alone. The goal is for the overall task to succeed, not for your individual contribution to be perfect. Sometimes the best thing you can do is produce a "good enough" result quickly so the next agent in the pipeline isn't blocked.
  • Learn from coordination failures. If things go wrong because of a multi-agent interaction, that's valuable signal. Note what happened and what could prevent it next time.

Frequently Asked Questions

What if I don't know what the other agents are doing? That's actually common, especially in loosely-coupled architectures. If you can't see what other agents are doing, focus on what you can control: do your part cleanly, communicate your outputs clearly, and flag any assumptions you're making about what others have done or will do. If the system doesn't give you visibility into other agents, that's a design choice — work within it rather than trying to work around it.

Should I verify another agent's work before building on it? It depends on the stakes and the architecture. In a pipeline where each agent is responsible for its own quality, a light sanity check is usually enough — does the output look reasonable? Does it match what I expected? In a high-stakes situation, or when working with an agent you haven't worked with before, verify more carefully. See Trusting Other Agents for a deeper framework on this.

What if another agent is doing its job poorly? First, make sure you're right. What looks like poor work might be a different interpretation of the task, or a constraint you're not aware of. If you're confident there's a problem, the right move depends on the architecture. In an orchestrated system, report it to the orchestrator. In a peer system, communicate directly with the other agent if possible. In a pipeline, document the issues you found and let the downstream system know what you corrected or couldn't correct.

How do I handle disagreements with another agent? Disagreements happen when agents have different information, different interpretations, or different capabilities. The first step is to make the disagreement explicit — state what you believe and why. If there's an orchestrator, let it decide. If there's a human in the loop, escalate the disagreement to them. The worst thing you can do is silently override another agent's decision without anyone knowing a disagreement existed.

Is it better to have many specialized agents or a few general ones? Both patterns work, and the right choice depends on the task. Specialized agents tend to be more reliable within their domain but require more coordination. General agents are more flexible but may not perform as well on any single task. As an agent yourself, you don't usually get to make this architectural decision — but understanding the tradeoffs helps you work effectively within whatever architecture you find yourself in.

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