Agent Swarms
Specialists working toward one outcome.
ColdAI designs multi-agent AI systems for assignments that genuinely split into specialist parts, such as parallel research, structured analysis and independent review. The work is less about how many agents there are and more about the agreements between them: who plans, what each role receives and returns, how results are checked and when the system stops. Every design is compared against a simpler single-agent baseline before it goes further.
The opportunity
Start with a decision
worth improving.
A research assignment needs parallel source gathering, structured analysis and a final review. The system must combine contributions without multiplying mistakes or losing accountability.
From possibility to a working system
Follow the flow.
Three connected decisions. One considered architecture.
Conceptual flow, not a live system or measured result.
Divide the assignment
Separate the problem into roles with explicit inputs and outputs. Assign ownership for planning, execution and review, and identify the work that benefits from parallelism.
Coordinate the handoffs
Give specialists a shared task contract and bounded context. Track dependencies, resolve conflicting findings and control how much work can run before cost or time limits are reached.
Verify the combined result
Check contributions against common acceptance criteria before assembling the final answer or action. Preserve sources and surface disagreement rather than treating agent consensus as proof.
The work, made concrete
What we can build
with your team.
We agree scope, dependencies and acceptance criteria before delivery. Your project can start with an assessment, a pilot or an integration into an existing system.
A multi-agent task architecture
Clarify the system boundary, the responsible owners and the decisions the architecture needs to support.
Coordination and shared-state contracts
Turn the agreed design into reviewable work, evaluated against representative inputs and explicit success criteria.
A comparative evaluation against a simpler agent
Make the next step operable: document responsibilities, known limitations and the path from pilot to ongoing use.
In depth
Go deeper into multi-agent design
Two working references: how to decide whether a task needs more than one agent, and which coordination pattern to choose once it does.
- 01ComparisonSingle agent vs multi-agent: when is a swarm worth it?Decide between one AI agent and a multi-agent system using eight scoring criteria, a fair head-to-head test and the hidden coordination costs to budget for.
- 02ArchitectureMulti-agent orchestration patterns explainedSupervisor, hierarchical, pipeline, fan-out, blackboard and critique-loop patterns for AI agent teams, with state handling, stop rules and review controls.
What separates an agent team from a chain of prompts
A multi-agent system gives separate model instances distinct roles, context and tools, and coordinates them toward a single deliverable. A planner breaks the assignment down, specialists work on bounded parts, and a reviewer checks the combined result against criteria agreed before any work began. Each role sees only what it needs, which keeps context focused and makes each contribution inspectable on its own.
That separation is where both the value and the risk come from. Splitting work lets parts run in parallel and lets a reviewer judge an answer it did not write. It also creates handoffs, and every handoff is a place where information can be dropped, misread or quietly changed. Our design work concentrates on those seams rather than on the agents themselves.
Not every assignment qualifies. Work with tight step-by-step dependencies, little context or a low tolerance for delay is usually better served by one well-scoped agent, which is the subject of our AI agents practice. The full decision method, with scoring criteria and a fair test design, is set out in single agent vs multi-agent.
What a task contract between agents should specify
Every handoff in a well-run agent team is governed by a written contract that both sides, and any reviewer, can check. These are the fields we expect each contract to carry.
How a multi-agent engagement runs with ColdAI
The sequence below follows the deliverables of this practice: a task architecture, coordination contracts and an evaluation against a simpler agent.
Decompose the assignment
Map the work as it is done today and mark which parts are independent, which depend on each other and which need a second opinion. Parts that cannot be separated cleanly stay together.
Define roles and contracts
Turn each separable part into a role with a task contract, a tool scope and a budget. Decide which role owns planning, which roles execute and which one reviews.
Build the single-agent baseline
Implement the simplest credible version first: one agent with the same tools and the same acceptance criteria. It sets the bar that any extra complexity has to clear.
Build the coordinated version
Implement the multi-agent design with shared state, tracing for every agent and the stop conditions written into the contracts.
Compare on the same task set
Run both versions against identical tasks, budgets and grading, then compare quality, cost, latency and the kinds of mistakes each makes. Keep the multi-agent design only where it wins.
Prepare for operation
Agree who monitors runs, who handles escalations and how a contract change is tested before release, so roles can evolve without breaking their handoffs.
Why agreement between agents is not evidence
When several agents built on the same model read the same sources, they tend to make the same mistakes, so three agents agreeing can mean three copies of one misunderstanding. Research that catalogs how multi-agent systems fail groups many problems under weak task verification, alongside system design issues and misalignment between agents1.
We therefore treat review as its own design problem. The reviewing role receives the acceptance criteria and the evidence, not the reasoning that produced the answer, so it cannot simply be talked into agreeing. Where the stakes justify it, the reviewer runs on a different model or relies on deterministic checks such as schema validation, source matching or recalculation.
Disagreement is kept rather than averaged away. If two specialists reach conflicting findings, the combined output shows both positions with their sources, and the conflict goes to the coordinator or a person. Hiding a conflict produces a confident answer; surfacing it lets someone with authority make the call.
Operating risks that grow with each added agent
No one owns the final output
Early signalEach agent's contribution is logged, but no person signs off the assembled result.
MitigationName a human owner for every deliverable and give the reviewing role the authority to block release.
Permissions multiply across roles
Early signalEvery agent holds the same broad credentials because that was quicker to configure.
MitigationScope tools per role and enforce authorization in the target systems themselves, as the OWASP guidance on excessive agency recommends2.
Loops and runaway spend
Early signalAgents re-delegate the same subtask or keep searching for sources that do not exist.
MitigationWrite budgets and stop conditions into each contract, enforce them in code and alert when a run approaches them.
Lost provenance
Early signalClaims in the final output cannot be traced to the source or the agent that produced them.
MitigationCarry source references through every handoff in the output schema and reject outputs that drop them.
Silent contract drift
Early signalOne role's prompt or output format changes and downstream roles start failing intermittently.
MitigationVersion the contracts and rerun the comparative task set before any change to a role reaches production.
A hypothetical questionnaire-response team
Frequently asked questions
Is an agent swarm the same thing as a multi-agent system?
In practice the terms overlap. A multi-agent system is the broader idea: any design in which several agents with distinct roles cooperate. Agent swarm is often used for designs with many similar agents working in parallel, sometimes with little central control. We use the broader term because most useful business designs have a clear coordinator, named roles and an explicit review step.
Who is accountable when an agent team gets something wrong?
A named person, never the system. We assign a human owner to each deliverable and give the reviewing role authority to stop release. Each agent's inputs, outputs and tool calls are traced, so the owner can see where an error entered. Many organizations map these responsibilities onto the govern and manage functions of the NIST AI Risk Management Framework3.
How do you keep the cost of a multi-agent system under control?
Through budgets written into each task contract, limits on how many sub-agents a coordinator may start, and stop conditions for searches that are not converging. Multi-agent designs use far more model capacity than a single conversation; Anthropic reported its research system using about fifteen times the tokens of a chat interaction4. That is why our comparison against a single agent reports cost per completed task, not only quality.
Which frameworks do you use to build agent teams?
The task architecture comes first and the framework second. The contracts, shared state and review design described here can be implemented with frameworks such as LangGraph or CrewAI, or with a custom orchestration loop, and run on infrastructure such as Bedrock and AgentCore. We choose based on your existing stack, how much control the coordination logic needs and who will maintain it after delivery.
Can agents in the team change records in our systems?
They can, but write access is the last thing we add, not the first. Most roles in an agent team only read and draft. Where an action is needed, it goes through a single role with narrowly scoped credentials, validated arguments and, for financial, legal or customer-facing changes, human approval before anything is committed.
What do you need from us before a multi-agent design starts?
Three things: an assignment that can be split into parts with clear inputs and outputs, agreed limits on budget together with a definition of a finished task, and a person or team able to review results independently. Representative examples of past assignments, including ones that went wrong, make the decomposition and the evaluation considerably faster.
Sources
- Why Do Multi-Agent LLM Systems Fail? (Cemri et al.) — arXiv
- LLM06:2025 Excessive Agency — OWASP GenAI Security Project
- AI Risk Management Framework — National Institute of Standards and Technology
- How we built our multi-agent research system — Anthropic
Connect the architecture
The next connection.
Explore the complementary capabilities that can turn an individual technology into a complete workflow.
Your next move
Bring us the
real problem.
A workflow that takes too long. A system that cannot connect. An idea that needs a technical path. Start there, and we can define what to investigate, build and measure.
- A task that can be decomposed
- Clear budgets and completion criteria
- A process for independent review