To set up a multi-agent handoff, split the job at the points where the work changes character, define a clear contract at each boundary stating what data and state pass and what "done" means, pass the finished sub-result plus the context the next agent needs, and validate that result on arrival before the receiving agent starts. A handoff is one agent passing a completed sub-result to another agent that then runs its own reasoning; it is not a single agent calling many tools to gather data for itself. Getting the boundaries and contracts right is what keeps a multi agent workflow reliable instead of fragile.
This guide walks through the setup step by step: deciding when to split, defining handoff points, writing the contract at each boundary, passing context and state without loss, validating success at each handoff, and handling errors and human checkpoints where the stakes are high.
What a multi-agent handoff is
A handoff happens when one agent finishes a self-contained piece of work and passes ownership of the job to a second agent, which picks up from there with its own instructions, tools, and reasoning loop. The boundary between them is the handoff point. What crosses that boundary is a contract: a defined sub-result, the state needed to continue, and an agreement about what "complete" means.
This is different from a single agent calling many tools. When one agent calls a tool, it asks for a piece of data or an action and gets a result back so it can keep reasoning. Control never leaves that agent. A handoff transfers control. The first agent is finished; the second is now responsible. That distinction matters because it changes what you have to design: with tool calls you design prompts and tool schemas, but with handoffs you design boundaries, contracts, and validation. If you want the underlying patterns in depth, agent handoff patterns covers the common shapes a handoff can take.
A multi-agent system is a set of these handoffs wired into a flow. Each agent does one part well, hands its output to the next, and the chain produces the finished job. The reliability of the whole system depends almost entirely on how clean each boundary is.
When to split a job across agents
The first decision is whether to split at all. One capable agent with the right tools can handle a surprising amount of work on its own, and every boundary you add is a boundary you have to define, pass state across, and validate. So split only when the separation earns its cost. Split a job across multiple agents when:
- The phases need different expertise. A research phase, a drafting phase, and a compliance review phase want different instructions, different tone, and sometimes different tools. Splitting lets each agent specialize instead of one agent trying to do all three with one prompt.
- The phases need different tools or permissions. An agent that reads from a database and an agent that sends external messages have different access. Keeping them separate keeps each agent's permission surface small, which is a cleaner safety boundary.
- One agent's context would get overcrowded. Stuffing every instruction and every intermediate result into one agent dilutes its focus. Handing off a finished sub-result lets the next agent start with a clean, relevant context.
- You want an independent check between phases. A separate agent validating the previous agent's output catches errors that the producing agent is blind to, because it reasons from the contract rather than from its own work.
- You want to reuse a step. An agent built to a clean contract can serve several different workflows, which is far easier than copying logic into one monolithic agent.
If none of these apply and the whole job fits one instruction set and one tool group, keep it in a single agent. When you do decide to split, the related guides on chaining agents for complex tasks and building a multi-step agent workflow help you map the full sequence before you wire individual boundaries.
Step 1: define the handoff points
A handoff point is the seam where one agent's job ends and the next begins. Place it where the work changes character: where the goal shifts, where the tools change, or where a natural "this part is finished" moment exists. A good handoff point produces a sub-result that stands on its own, something you could describe in one sentence and check without re-running the previous steps.
Map the job end to end first, then mark the seams. For a content workflow that might be: gather sources, draft, fact-check, format. The seams between those four phases are your candidate handoff points. For each one, ask whether the output is genuinely a finished unit. "A draft with citations attached" is a clean sub-result; "a half-formed set of notes the next agent has to interpret" is not. If a candidate boundary leaves the next agent guessing, move the boundary or merge the two steps.
Aim for the fewest boundaries that still give you the specialization, safety, or reuse you wanted. Every handoff point is a place where information can be dropped, so each one needs to justify itself. Fewer, cleaner boundaries beat many leaky ones.
Step 2: write the contract at each boundary
The contract is the agreement at a handoff point. It states what the sending agent must produce and what the receiving agent is allowed to assume. A clear contract is what lets you build, test, and change each agent on its own: as long as it keeps honoring the boundary, the rest of the chain does not care how it works inside. A complete contract specifies four things:
- The data shape. Exactly what fields and structure the sub-result has. If the draft agent promises a title, body, and a list of source URLs, the fact-check agent can rely on those fields being present and well-formed.
- The state and context that travel with it. Beyond the sub-result itself, what does the receiver need to continue? The original request, key decisions made, and any constraints carried forward. We cover what to pass in step 3.
- The definition of done. What makes the sub-result complete and ready to pass. "Done" for the draft agent might mean every claim has a source attached and the word count is within range. Without an explicit definition of done, agents hand off partial work that looks finished but is not.
- The success criteria the receiver checks. The conditions the receiving agent validates on arrival before it starts. These mirror the definition of done but are enforced on the other side of the boundary, so a broken handoff is caught immediately.
Write the contract as plain, explicit rules, not as an implicit understanding. The most common cause of a broken multi-agent system is two agents that each assumed the other handled something. A contract removes the assumption.
Step 3: pass context and state cleanly
The receiving agent does not automatically see what the sending agent saw. Each agent runs its own reasoning loop with its own working context, so anything the receiver needs has to be passed explicitly as part of the handoff payload. This is where most context loss happens: the first agent knew the original request and made decisions along the way, then handed over only the final artifact, and the second agent has to guess at the rest.
Pass three things across the boundary: the finished sub-result, the inputs and decisions that produced it, and the state the receiver needs to continue. For the content example, that means the draft, the original brief and any editorial decisions, and a record of which sources were used. The receiver should not have to rebuild context from scratch or re-derive what the previous agent already settled.
The cleanest way to do this is a shared record of the run that every agent reads from and writes to, so there is one source of truth rather than context copied agent to agent. This is the same discipline covered in agent state management: keep state external and explicit so any agent can pick up where the last one left off. Pass what the receiver needs, not the entire history. Dumping every prior message into the next agent's context crowds out its focus and is the opposite of a clean handoff. If your agents also carry knowledge across separate runs, building an agent with memory covers the longer-lived layer that sits alongside per-run state.
Step 4: validate at each handoff
A handoff without validation is a handoff that trusts the previous agent was perfect. It was not always. The receiving agent should check the incoming sub-result against the success criteria in the contract before it starts working. This validation is the safety gate of the whole chain.
Validation can be simple and structural: are the required fields present, is the data the right shape, does the sub-result meet the definition of done. It can also be substantive: does the draft actually cover the brief, are the cited sources real, is the output within the constraints that were set. The point is that the receiver does not assume the handoff is good; it confirms it.
When validation passes, the receiving agent proceeds. When it fails, the handoff is rejected and the chain routes to recovery rather than pushing bad data downstream, which is the subject of the next step. Validating at the boundary is what stops a small error in an early agent from compounding into a wrong final result. Because the same agent that produced the work cannot reliably grade it, an independent receiver checking against the contract is a meaningful guardrail; the broader practice is covered in agent safety and guardrails.
Step 5: handle errors and human checkpoints
Boundaries are where you handle failure, because a boundary is already a natural checkpoint. Decide, for each handoff, what happens when things go wrong, and treat partial failure as a first-class case, not an afterthought.
There are two failure modes to plan for. The first is a sending agent that cannot produce a valid sub-result: it should stop and surface the failure rather than hand off whatever it managed to assemble. The second is a receiving agent whose validation fails on arrival: it should reject the handoff and route to recovery. Recovery can be a retry, a fallback to a simpler agent or path, or escalation to a human. Designing alternate paths in advance is its own discipline, covered in setting up agent fallback chains.
Because state is recorded at each boundary, you can resume from the last good checkpoint instead of restarting the entire job. A failure in the fact-check agent does not throw away the gathered sources and the draft; the chain can re-run from that boundary with the prior work intact.
Where the stakes are high, insert a human checkpoint at the boundary. Before an agent sends an external message, commits a payment, or publishes something public, a handoff is the right place to pause for approval: the producing agent's output is complete and reviewable, and the receiving agent has not yet acted on it. This is the safest spot to put a person in the loop, and adding a human in the loop to an agent walks through how to structure that approval step so it gates the right actions without slowing down the routine ones.
How Gravity handles multi-agent handoff
Gravity is an AI agent platform. You describe the job in plain words, and the right expert-built agent runs it end to end, handing back the finished result in about 60 seconds. When a job spans several specialized phases, the handoffs between agents are part of what the builder designs and maintains for Gravity: the boundaries, the contracts, the state that passes, and the validation at each step are built in, not something you wire together yourself.
That means you get the benefit of a clean multi-agent design, where each phase is handled by an agent suited to it, without owning the plumbing. The contract at each boundary, the context that travels with the work, and the checkpoints where validation or human approval belong are the builder's responsibility, tuned and maintained over time. You describe the outcome you want; the platform runs the chain that produces it. Pay per use: $1 equals 1,000 credits, and you only pay when an agent runs.
If you are still mapping out whether your job needs one agent or several, start from the basics in what is an AI agent and the glossary, then return to the steps above to define your boundaries. Multi-agent handoff is worth setting up when the job genuinely has distinct phases; when it does, clean boundaries are what make the whole thing dependable.
FAQ
What is the difference between a handoff and a single agent calling tools?
A tool call is one agent reaching out for a piece of data or an action and getting a result back to continue its own reasoning. A handoff is one agent finishing a complete sub-result and passing ownership of the work to a different agent, which then runs its own reasoning loop. The first stays inside one agent's context; the second crosses a boundary where context, state, and responsibility transfer. You use a handoff when sub-jobs need different skills, different tools, or independent validation, not just when a single step needs external data.
When should I split a job across multiple agents instead of using one?
Split when the job has distinct phases that need different expertise, tools, or permissions, when one agent's context would get too crowded to reason well, or when you want an independent check between phases. If the whole job fits one clear instruction set and one tool group, keep it in a single agent. Adding a handoff adds a boundary you have to define and validate, so split only when the separation buys you clarity, safety, or reuse.
What should the contract at a handoff boundary include?
The contract defines what the sending agent must produce and what the receiving agent can rely on. It specifies the shape of the data passed, the state and context that travel with it, the definition of done that marks the sub-result as complete, and the success criteria the receiver validates on arrival. A clear contract means each agent can be built, tested, and changed on its own as long as it keeps honoring the boundary.
How do I keep the receiving agent from losing context?
Pass context explicitly as part of the handoff payload rather than assuming the next agent can see what the previous one saw. Include the finished sub-result, the relevant inputs and decisions that produced it, and any state the receiver needs to continue. Keep a shared record of the run so each agent reads and writes to the same source of truth instead of rebuilding context from scratch. Pass what the receiver needs, not the entire history, so its working context stays focused.
What happens if an agent fails partway through a handoff chain?
A well-designed chain treats each boundary as a checkpoint. If a sending agent cannot produce a valid sub-result, it stops and surfaces the failure instead of passing bad data forward. If a receiving agent's validation fails on arrival, it rejects the handoff and routes to a retry, a fallback, or a human. Because state is recorded at each boundary, you can resume from the last good checkpoint rather than restarting the whole job.