Graph Engineering for AI Agents: A Practical Guide to Task Assignment, Multi-Agent Coordination & Handoffs
A useful mental model is a directed graph: nodes represent work, checks, decisions, approvals, or state transitions; edges describe what can happen next. Some routes can be fixed in code. Others can be selected by an agent at runtime. Some tasks can run in parallel. Others must wait for a dependency. A specialist may receive a task without becoming the user-facing owner, or it may take over through a true handoff. The exact design depends on the architecture.
Imagine a school project with four students. One student decides what needs to be done. Another checks the numbers. Another writes the explanation. A fourth checks the final work. The important part is not simply having four students. You need to know who does what, what they need, when they can start, what they must return, and who is allowed to make the final decision. A workflow graph makes those relationships explicit.
Consider a fictional enterprise invoice workflow. An invoice arrives. One agent extracts the important fields. Several checks can happen at the same time: purchase-order matching, policy validation, and supplier-data verification. A coordinator waits for the required checks, decides whether the case is routine or exceptional, and possibly sends it to an exception specialist. A human approval may be required before an irreversible action. Finally, the system records the outcome.
That scenario contains nearly every important graph-engineering question: task assignment, dependencies, parallel work, joins, routing, handoffs, state, permissions, recovery, observability, and completion criteria.
- What an Agent Workflow Graph Actually Controls
- How to Assign Tasks to the Right Agent
- How Multiple Agents Coordinate Work
- Handoffs: Moving Responsibility from One Agent to Another
- State, Context, and the Handoff Packet
- Failure, Retry, Cancellation, and Recovery
- Security: Permissions, Untrusted Inputs, and Blast Radius
- Testing and Evaluating the Graph
- Tracing and Operating the Workflow in Production
- Enterprise Rollout
- Common Mistakes
- ❓ FAQ
- 🔗 References & Further Reading
- 📝 Summary
| Pattern | Who chooses the next step? | Who keeps control? | Typical use |
|---|---|---|---|
| Fixed graph | Application code or workflow engine | Graph runtime | Predictable business processes |
| Agents as delegated workers | Coordinator | Coordinator | Specialists return bounded results |
| Handoff | Current agent or routing logic | Receiving agent after transfer | A specialist should own the next conversation or task phase |
| Hybrid graph | Some routes fixed, some model-selected | Depends on the node | Enterprise workflows needing both control and flexibility |
Current agent tooling illustrates these architectural differences rather than eliminating them. For example, the OpenAI Agents SDK documents both a manager pattern in which specialized agents are exposed as tools and a handoff pattern in which a specialist becomes the active agent. Anthropic's published agent guidance likewise distinguishes predefined workflows from systems in which the model dynamically directs its process. These are architecture patterns, not universal definitions of every agent platform.
01
What an Agent Workflow Graph Actually Controls
Before assigning agents, separate the major parts of the system. Many designs become confusing because the word “agent” is used for almost everything.
| Component | Main responsibility | Question to ask |
|---|---|---|
| Model | Produces predictions or structured outputs used by the application. | What reasoning or generation capability is being requested here? |
| Agent | Combines a model with instructions, tools, state, and runtime behavior for a task or role. | What responsibility does this agent actually own? |
| Workflow graph | Defines dependencies, routes, transitions, joins, stop conditions, and recovery paths. | What can happen next, and what must be true before it happens? |
| Harness | Runs and controls the agent loop, tools, limits, state handling, approvals, or other runtime behavior. | What runtime controls stop the agent from operating outside its contract? |
| Tool | Performs an external or local operation such as retrieving data or writing a record. | What real-world authority does this operation expose? |
| Sandbox | May isolate code, files, processes, or workspace activity. | What execution environment is isolated, and what remains outside it? |
Think of a restaurant. The model is like a person capable of suggesting what to do. The agent is a worker with a job and tools. The graph is the restaurant's sequence of work: order → kitchen → quality check → serving. The harness is the manager enforcing operating rules. A sandbox is a controlled kitchen area. Mixing these ideas leads to designs where nobody knows who is really in charge.
A workflow graph therefore should answer five basic questions:
- What is the task? Define a meaningful unit of work.
- What does it depend on? Identify prerequisites instead of relying on hidden assumptions.
- Who or what performs it? The worker might be an agent, deterministic function, service, database query, or human.
- What makes it complete? Define evidence or a measurable completion condition.
- What happens when it fails? Specify retry, fallback, escalation, cancellation, or compensation.
An agent should not be the only place where workflow truth lives. Important dependencies, permissions, limits, and completion rules belong in enforceable system controls.
You have more than one meaningful stage, decision, or participant and need to explain why the system took one route rather than another.
02
How to Assign Tasks to the Right Agent
Task assignment sounds simple: “Send this work to the invoice agent.” In a production graph, that sentence is incomplete. A useful task assignment defines both responsibility and boundaries.
A teacher does not say, “Take care of the project.” A better instruction is: “Check the three calculations, write down whether each one is correct, and return the results to me.” The second assignment has a clear job, clear input, and clear answer.
A practical task contract can contain:
This is deliberately architecture-neutral. It is not a vendor SDK configuration. It is a way to think about the contract before choosing a framework.
The most important design decision is task granularity. Make a task too large and the agent receives too much responsibility. Make it too small and the graph becomes expensive to coordinate and difficult to understand.
| Task design | Advantage | Typical problem |
|---|---|---|
| Too broad | Fewer graph nodes. | Hidden decisions, unclear authority, difficult testing. |
| Well-bounded | Clear ownership and easier verification. | Requires deliberate contracts. |
| Too granular | Fine-grained control. | Coordination overhead, more handoffs, more failure points. |
The intake stage extracts the invoice identifier, supplier identifier, purchase-order reference, and line items. Instead of asking one agent to “validate the invoice,” the graph creates separate responsibilities: PO matching, policy checking, and supplier-data verification. Each specialist receives only the information it needs and returns a bounded result.
Risk → A specialist is given unnecessary write permissions simply because it may eventually be involved in the workflow.
Control → Give the task only the tools and permissions required for its current responsibility. Separate read, propose, and commit operations where practical.
Remaining risk → A permitted read operation can still expose sensitive information, and a compromised agent may still misuse every permission it legitimately receives.
You are deciding whether a responsibility deserves its own agent, deterministic function, or ordinary application step.
03
How Multiple Agents Coordinate Work
Multiple agents do not automatically form a useful multi-agent system. Coordination requires a shared understanding of dependencies, outputs, timing, and ownership.
Imagine three detectives investigating the same mystery. Detective A checks the security log. Detective B checks the purchase record. Detective C checks the time stamps. They do not need to stand in a line. They can work at the same time, then bring their notes back to one person who compares them.
This is the graph pattern known as parallel work followed by a join.
The join is important. If the final decision requires all three checks, the graph should not silently continue after only one result arrives. The runtime needs a rule such as wait for all required branches, or an explicit partial-result policy if some branches are optional.
Coordination can happen in several ways:
| Coordination mode | Mechanism | Design concern |
|---|---|---|
| Sequential | One node finishes before another starts. | Simple but can add avoidable latency. |
| Parallel | Independent tasks start together. | Requires careful join semantics and concurrency limits. |
| Conditional | A condition selects one or more routes. | The condition must be observable and testable. |
| Loop | A node repeats until a stopping rule is met. | Requires hard limits to prevent runaway execution. |
A coordinator should not merely concatenate agent outputs. It should know which outputs are authoritative, which are advisory, which conflicts require another check, and which results satisfy the workflow's completion condition.
Suppose the PO specialist reports “match,” the policy specialist reports “within policy,” and the supplier specialist reports “supplier reference differs.” The graph should not blindly average the three answers. It should route the disagreement according to a predefined decision rule. Perhaps the supplier discrepancy creates an exception branch. The graph turns three independent results into one controlled next state.
Coordination quality comes from explicit contracts between nodes, not from increasing the number of agents.
Risk → Several agents independently trigger writes, creating duplicate or conflicting side effects.
Control → Keep side effects behind a controlled commit stage, use idempotency where appropriate, and make one component responsible for the final action.
Remaining risk → An idempotency key does not make an unsafe operation safe. A bad decision can still be committed once.
Tasks are independent enough to overlap, but a later decision needs their results together.
04
Handoffs: Moving Responsibility from One Agent to Another
The word handoff is often used loosely. A specialist receiving information is not necessarily a handoff. A handoff is most useful when responsibility or conversational control actually moves from one participant to another.
Think of a hospital reception desk. The receptionist may collect your details and then say, “The specialist doctor will take over from here.” That is different from the receptionist asking a doctor one small question and continuing to manage your visit. One is a transfer of responsibility; the other is delegated help.
Three concepts are worth separating:
| Concept | Meaning | Example |
|---|---|---|
| Delegation | A coordinator asks another agent to perform a bounded task and receives a result. | “Check whether these line items match the PO and return the findings.” |
| Handoff | The current responsibility moves to another agent. | “This is a payment exception; the exception specialist now owns the case.” |
| Artifact passing | One node passes data without transferring control. | The policy checker returns a structured result to the coordinator. |
Current OpenAI Agents SDK documentation explicitly describes both patterns: “agents as tools,” where a manager keeps control, and “handoffs,” where the receiving specialist takes over. Its handoff mechanism also supports controlling what history is passed onward. That is a useful concrete example of why “multi-agent” and “handoff” should not be treated as synonyms.
A robust handoff has at least these fields:
Notice that the handoff carries more than prose. It carries state, evidence, authority, and a reason for transfer. That makes it easier to audit and test.
The intake agent does not tell the exception specialist, “Something looks wrong.” It creates a structured case: which check failed, what evidence supports the finding, which checks already passed, what the specialist may do, and what it may not do. The specialist begins from a known state instead of reconstructing the entire investigation.
Risk → A receiving agent trusts every statement included in a handoff and treats it as authoritative.
Control → Distinguish facts, retrieved evidence, agent interpretations, and pending decisions. Validate critical fields before a consequential action.
Remaining risk → A correctly structured handoff can still contain an incorrect interpretation. Structure improves control; it does not create truth.
A specialized agent should genuinely own the next phase rather than merely provide a small result to a coordinator.
05
State, Context, and the Handoff Packet
A handoff raises an easy question: What exactly does the next agent receive? This is where graph engineering overlaps with context engineering, but the two should not be confused.
Context engineering concerns selecting and organizing information that the model receives for a task. Graph engineering concerns how work moves through the system. The graph may decide which context package is appropriate for the next node, but context selection itself is not the whole graph.
Imagine passing a folder to the next person. The workflow decides who gets the folder. Context engineering decides which papers are inside it. State management decides where the official folder is stored.
A useful state design separates at least three categories:
| State type | Examples | Design question |
|---|---|---|
| Workflow state | Current node, completed checks, pending branches, retry count. | Can the workflow resume safely from this point? |
| Business state | Invoice status, approval status, case state. | What is the authoritative source of truth? |
| Model context | Relevant messages, evidence, summaries, retrieved records. | What information is actually needed for the next decision? |
Do not assume that conversation history is equivalent to authoritative state. A transcript can say that an agent believes a purchase order matched. The system of record should establish whether it actually matched.
Likewise, do not assume that passing more history produces a better handoff. A receiving agent may need the relevant findings, not every intermediate thought, tool response, or unrelated conversation detail.
Instead of passing the entire invoice conversation, the exception specialist receives the case identifier, structured validation results, the source records required to verify the mismatch, the current workflow state, and a bounded set of allowed actions. The graph remains readable and the next agent starts with a purposeful context package.
Store durable truth outside the model's temporary reasoning context whenever the application needs reliable recovery or auditing.
Your workflow can pause, resume, retry, move across services, or involve humans between agent steps.
06
Failure, Retry, Cancellation, and Recovery
A graph is not production-ready because the happy path works. Production engineering starts when the network fails, a tool times out, a specialist returns an invalid result, a worker repeats a write, or a human never approves the requested action.
Suppose a delivery driver gets a flat tire. A good delivery system does not say, “The delivery failed, start everything from the beginning.” It knows what has already happened, whether the package is safe, whether another driver can continue, and whether the original attempt changed anything.
For each node, ask four questions:
- Can it be retried? A read-only operation may be retriable; a committed payment may not be.
- Is the operation idempotent? Repeating it should not unintentionally create duplicate effects.
- Can execution be cancelled? A loop or long-running external operation needs a defined stop path.
- Where does recovery resume? Restart from the beginning only when the earlier steps are safe to repeat.
OpenAI's current Agents SDK documentation is one example of a runtime that models execution as a loop: the current agent produces output, a handoff can change the active agent, tool calls can be executed, and a run can terminate on final output or a configured turn limit. The important graph-engineering lesson is broader than that implementation: execution must have explicit stopping and continuation rules.
The last point matters. A missing response is not the same thing as approval.
Risk → A retry mechanism repeats a consequential action.
Control → Classify operations by side-effect risk, use bounded retries, record operation identifiers, and separate “attempted” from “committed” state.
Remaining risk → External systems may have ambiguous outcomes. A timeout does not always prove that an operation did not happen.
Suppose the graph requests a supplier lookup and the service times out. The graph records that the lookup was attempted but the result is unknown. It does not manufacture “supplier not found.” A retry may be attempted within a bounded budget. If ambiguity remains, the workflow moves to a review state rather than guessing.
The workflow touches external systems, performs writes, can run for a long time, or may be interrupted between nodes.
07
Security: Permissions, Untrusted Inputs, and Blast Radius
Multi-agent coordination creates a security problem that ordinary request-response software often has in a simpler form: one workflow can accumulate access to multiple tools, data sets, and systems. A failure in one node may therefore propagate through another.
NIST's 2026 work on AI-agent security highlights risks created by combining model outputs with software capabilities. Its work on agent identity and authorization also emphasizes that agents need appropriate identification, authentication, authorization, auditing, and related controls. OWASP's 2026 agentic guidance similarly treats excessive agency, tool misuse, insecure inter-agent communication, cascading failures, and related issues as significant concerns for agentic systems.
Imagine giving one student the keys to the classroom, office, laboratory, and school bank simply because the student might need to ask someone a question. The student's role may be helpful, but the key ring is much larger than the job.
For graph engineering, least privilege should be considered at the node and action level, not merely at the application level.
| Graph layer | Security control | Example question |
|---|---|---|
| Routing | Restrict which destinations are available. | Can this agent route directly to a privileged node? |
| Context | Minimize sensitive data and validate untrusted content. | Does the next agent really need this field? |
| Tools | Use narrow operations and authorization checks. | Is this a read, proposal, or commit operation? |
| Handoffs | Authenticate participants and validate handoff data. | Can a message cause the receiving agent to gain authority it should not have? |
| Commit | Require stronger controls for irreversible actions. | Does this action need a human or policy gate? |
Treat external content as potentially untrusted. An email, document, retrieved page, tool response, or message from another agent can contain text designed to influence the next decision. The graph should therefore avoid the assumption that “another agent said it” means “it is trusted.”
Risk → An untrusted document or agent message influences a privileged node, which then performs a high-impact action.
Control → Validate critical inputs, separate untrusted evidence from control instructions, restrict available tools, and place high-impact actions behind explicit authorization or human review when appropriate.
Remaining risk → No single prompt filter, approval step, or sandbox eliminates every form of manipulation. Defense in depth is required because different controls protect different failure modes.
A useful way to think about blast radius is to trace the worst possible path through the graph. Ask:
- What untrusted information can enter?
- Which nodes can be reached from that input?
- Which tools can those nodes call?
- Which operations can change durable state?
- Where can a human or policy stop the sequence?
This produces a more useful security review than simply asking whether an individual prompt “looks safe.”
Design the graph so that a compromised or mistaken agent has limited authority and limited routes to high-impact actions.
Agents can access customer data, credentials, production systems, financial operations, communication systems, or other high-impact tools.
08
Testing and Evaluating the Graph
Evaluating a multi-agent workflow is different from evaluating a model in isolation. The graph can fail even when every individual response looks reasonable.
A relay race can fail even when every runner is fast. The baton can be passed to the wrong runner, dropped between runners, or carried beyond the finish line. In a workflow graph, the “baton” is state and responsibility.
Test the graph at several levels:
| Test level | What to test | Example failure |
|---|---|---|
| Route test | Correct path selection for known cases. | A policy exception takes the routine route. |
| Contract test | Node inputs and output schema. | A worker returns an ambiguous status that the next node interprets as approval. |
| Handoff test | Correct destination, context, state, and permissions. | Receiving agent gets the wrong case or excessive authority. |
| Failure test | Timeouts, invalid output, cancellation, retries. | Retry creates a duplicate side effect. |
| Security test | Authorization boundaries and untrusted inputs. | A low-privilege node reaches a commit tool. |
| Completion test | Whether the graph really stops only when requirements are satisfied. | The agent says “done” although one required check is missing. |
A valuable test strategy is to create route fixtures: small synthetic cases whose expected paths are known. For example:
Then test not only the final result but the route trace. The question becomes: “Did the workflow reach the right conclusion through the right controls?”
This is especially important when a model is allowed to choose among tools or routes. A correct final answer produced through an unauthorized path is still a workflow failure.
Risk → Tests check only successful outputs and never exercise denied actions, malformed state, or partial failures.
Control → Include negative tests: unauthorized routes, malformed handoffs, missing evidence, repeated calls, stale state, cancellation, and unexpected external content.
Remaining risk → Test suites are samples. Production behavior can still expose unseen combinations and integration failures.
You need confidence that the workflow behaves correctly under both normal and adversarial conditions, not merely that individual agents generate plausible text.
09
Tracing and Operating the Workflow in Production
When a graph contains several agents, a single application log line such as “request completed” tells you very little. Production troubleshooting requires a trace that shows how the workflow moved.
Suppose a package is delayed. “Package failed” is not enough. You want to know: where it started, which truck carried it, where it stopped, why it stopped, whether it was rerouted, and who finally delivered it. A workflow trace is the equivalent journey record.
A useful graph trace can capture:
- Run identifier connecting all events belonging to one workflow.
- Node transitions showing which task started and finished.
- Agent identity indicating which logical worker acted.
- Handoff events showing where responsibility changed.
- Tool events showing consequential external operations.
- State transitions showing what the workflow believed to be true.
- Budget signals such as time, retries, or other bounded execution resources.
- Outcome including completion, escalation, cancellation, or failure.
Do not automatically log everything. Logs can become a security problem when they contain credentials, personal data, private prompts, document contents, or sensitive business records. Observability should be designed alongside retention and access controls.
A trace shows: intake → three parallel checks → join → exception specialist → human approval → commit. The commit occurred only after approval. If a production incident later reveals an unexpected write, the trace lets the team determine which node invoked the action and whether the graph followed the expected route.
For an agent workflow, observability should make the execution path explainable to an engineer without requiring the engineer to guess what happened.
The workflow matters enough that an operator may need to explain, reproduce, recover, or investigate its behavior.
10
Enterprise Rollout
An enterprise workflow graph should be treated as software and operational policy, not as a collection of prompts stored in an application repository.
A practical rollout can follow this sequence:
- Start with a narrow business process. Choose a workflow with measurable inputs, outputs, and boundaries.
- Model the graph before selecting agents. Mark deterministic steps, agentic steps, human gates, external systems, and failure paths.
- Write node contracts. Document inputs, outputs, permissions, completion conditions, budgets, and retry behavior.
- Test route behavior. Include successful, partial, malformed, unauthorized, and interrupted cases.
- Instrument from the beginning. Give every workflow execution an identifier and trace its important transitions.
- Introduce production authority gradually. Begin with read-oriented or recommendation tasks where practical; add write authority only after controls and recovery have been tested.
- Assign ownership. Someone should own the workflow definition, someone should own connected services, and security or risk teams should review high-impact permissions.
- Control change. Treat route changes, new tools, new handoff targets, and permission changes as production changes requiring review.
A graph becomes considerably easier to govern when the organization can answer: Which version ran? Which agent had which tools? Which route was taken? Which external action occurred? Which human decision was required? What state was recorded?
Risk → The graph evolves faster than its security and operational controls.
Control → Version workflow definitions, review permission changes, retain appropriate audit records, and test rollback or recovery procedures.
Remaining risk → Governance processes cannot prevent every software defect. They reduce the chance that an unsafe change reaches production unnoticed.
The simplest enterprise architecture is often the best starting point: a small number of well-defined nodes, explicit state, narrow tools, deterministic gates for important decisions, and clear escalation paths. Complexity should be earned by a real requirement.
The workflow crosses team boundaries, touches regulated or sensitive data, or is becoming important enough that a production incident would require formal investigation.
11
Common Mistakes
The following mistakes repeatedly make multi-agent graphs harder to trust and operate.
| Mistake | Why it fails | Correction |
|---|---|---|
| One giant “manager” agent | It owns too many decisions, tools, and context sources. | Split responsibilities where there is a real boundary and keep deterministic rules outside the model where appropriate. |
| Too many agents | Coordination overhead grows without a meaningful separation of responsibility. | Use the simplest graph that satisfies the business requirement. |
| Every agent can call every tool | A compromised or mistaken route can reach unnecessary authority. | Apply least privilege per node or action. |
| Handoff means “send the whole history” | The next agent receives irrelevant, sensitive, or misleading context. | Pass a deliberate handoff packet and authoritative state. |
| Agent says “done” | The model's declaration is treated as proof of completion. | Use explicit completion criteria and evidence. |
| Retry everything | Retries may duplicate side effects. | Classify operations and retry only where the semantics are understood. |
| No cancellation path | Loops and stalled tasks can consume resources indefinitely. | Set bounded execution budgets and define safe cancellation behavior. |
| Only happy-path tests | The graph's most dangerous failures remain invisible. | Test denied access, malformed handoffs, stale state, timeouts, duplicate calls, and partial branches. |
| Approval fatigue | Humans begin approving prompts mechanically. | Reserve approval gates for meaningful decisions and present concise, decision-relevant evidence. |
The common theme is overloading the model with responsibilities that should instead be made explicit in the graph, runtime, authorization layer, or application.
A graph should make important behavior easier to inspect, constrain, and test than an equivalent monolithic agent.
12
❓ FAQ
No. A simple agent can be sufficient when one agent can complete the task with a small number of tools and no meaningful coordination problem exists. A graph becomes valuable when dependencies, branching, parallel work, handoffs, approvals, recovery, or explicit completion rules need to be controlled.
When an agent is delegated as a bounded tool, the coordinator normally keeps responsibility for the overall interaction and combines the specialist's result. In a true handoff, responsibility moves to the receiving agent for the next part of the workflow. The distinction matters because it affects ownership, context, tracing, and authorization.
No. The receiving agent should receive the information needed for its responsibility, plus the authoritative workflow or business state required to act correctly. Some frameworks may make conversation history easy to forward, but that behavior is architecture-specific rather than a general graph-engineering rule.
Use an agent where interpretation, flexible reasoning, or model-driven decisions provide meaningful value. Prefer deterministic code for rules that must be exact, repetitive state transitions, authorization checks, irreversible commits, and other logic that is easier to express and test without probabilistic generation.
Not automatically. More agents create additional coordination points, context transfers, permissions, latency, cost, and failure modes. Reliability comes from clear responsibilities, controlled transitions, explicit state, bounded authority, testing, and observable completion conditions.
13
🔗 References & Further Reading
- OpenAI Agents SDK — Agent orchestration: documents manager-style orchestration using agents as tools and handoff-based orchestration.
- OpenAI Agents SDK — Handoffs: documents handoff behavior, handoff inputs, filtering, authorization considerations, and control transfer.
- OpenAI Agents SDK — Running agents: documents agent execution loops, state, sessions, and resumption concepts.
- Anthropic — Building effective agents: provides a published distinction between workflows and agents and discusses orchestrator-worker and other agentic patterns. The page itself notes that the tooling landscape described in its 2024 article has evolved.
- NIST — Summary Analysis of Responses to the RFI on Security Considerations for AI Agents: 2026 publication covering security concerns and mitigation themes for AI-agent systems.
- NIST NCCoE — Software and AI Agent Identity and Authorization concept paper: focuses on identification, authentication, authorization, auditing, and related controls for software and AI agents.
- OWASP GenAI Security Project — Top 10 for Agentic Applications 2026: current security guidance for agentic applications, including risks and control themes relevant to tool use, agency, communication, and cascading effects.
Product and organization names remain the property of their respective owners. The invoice scenario is fictional and is included only as a teaching example.
14
📝 Summary
- Assign tasks by responsibility, inputs, outputs, authority, limits, and completion criteria.
- Use parallel branches only when the tasks are sufficiently independent, and define how their results join.
- Distinguish delegation from a true handoff: a delegated specialist can return a result while a handoff transfers responsibility.
- Keep workflow state, business truth, and model context conceptually separate.
- Design retries, cancellation, recovery, and completion rules before production rather than after the first failure.
- Give each node only the authority it needs and treat external and inter-agent content as potentially untrusted.
- Evaluate routes, handoffs, state recovery, permissions, and execution traces—not only the final model output.
The core lesson is simple: a multi-agent system becomes easier to engineer when responsibility is represented explicitly. The graph should make it clear what happens, who does it, what information moves, what authority is available, what proves completion, and what happens when something goes wrong.
Comments
Post a Comment