Multi-Agent System Development: Stop Building Agents—Build Coordination

Most multi-agent demos don’t fail because the agents are “dumb.” They fail because two smart agents can still create a dumb system—through bad handoffs, conflicting writes, and unrecoverable partial progress.

That’s the uncomfortable truth at the center of multi-agent system development: model capability is rarely the limiting factor in production. Coordination is. If you’re building LLM agents that touch real tools—CRMs, ticketing systems, payments, internal docs—you’re building a distributed system with language as the interface.

This is a production guide, not a prompt gallery. We’ll focus on the primitives that make agentic workflows reliable: explicit contracts for task handoff, an orchestration layer with state you can trust, conflict resolution mechanisms that handle shared resources, coordination testing that simulates real failure modes, and observability that makes “AI weirdness” debuggable.

By the end, you’ll have a coordination-first playbook: architectures, protocols, conflict controls, reliability tests, and operational guardrails that survive handoffs, conflicts, and load. At Buzzi.ai, we build agentic workflows for businesses with coordination, verification, and ops baked in—because in production, “works once” is the same as “doesn’t work.”

Why multi-agent systems fail: coordination beats capability

If you’ve ever watched a multi-agent prototype impress in a demo and then quietly implode in production, you’ve already met the villain: coordination failure modes. These aren’t exotic. They’re mundane distributed-systems problems wearing an LLM costume—race conditions, ambiguous ownership, inconsistent state, and retries that create duplicates.

When you add more agents, you’re not just adding more “brains.” You’re increasing coordination surface area. Without management, the number of interactions between agents can grow roughly like O(N²), not because of math purity, but because everyone can step on everyone else’s toes.

The demo trap: linear scripts masquerading as systems

Demos succeed for the same reason “Hello, World” succeeds: it’s a single happy path. There’s no contention. There are no retries. Latency variance doesn’t exist. The input is clean. The tools always respond. The system never restarts mid-flight.

Production is the opposite. You have parallel requests. You have partial failures. You have evolving inputs. You have systems-of-record that reject writes. And you have humans who do things “out of band,” which means your agent reads stale data and confidently acts on it.

Here’s a failure story you can probably recognize. A team builds an agent workflow automation prototype for support. One agent summarizes the conversation and proposes a resolution; another agent closes the ticket if it sees “resolved.” In staging, everything looks perfect. In production, two inbound messages arrive within seconds, both mapped to the same ticket. Both runs decide the ticket is resolved. Both attempt to close it and send the customer a “We’ve closed your case” message. One close succeeds, one fails, and now the second run retries—reopening the ticket due to a bad tool call fallback. Customers are confused, and support loses trust in the automation.

Nothing about that problem is “LLM intelligence.” It’s coordination: ownership, ordering, idempotency, and safe retries.

Three coordination taxes you always pay (state, time, incentives)

In distributed decision-making, there are three taxes you pay whether you acknowledge them or not.

State is the obvious one. If multiple agents can read or write the same resource—ticket, invoice, document—you need rules for consistency: who can write what, when, and under which preconditions. Otherwise, you get race conditions, overwritten fields, and “last write wins” disasters.

Time is the sneaky one. Real systems have timeouts and retries. Tools return 500s. Queues delay work. Messages arrive out of order. If you don’t design for this, you get unrecoverable partial progress: half a workflow completed, the other half lost, and no clean rollback and recovery path.

Incentives is the human one. Even if all agents are “aligned,” they can optimize locally. A fast agent might skip verification to reduce latency. A compliance-focused agent might block everything. If you don’t define global correctness and acceptance criteria, you’re letting emergent behavior run your business process.

This is why the right analogy isn’t “a smart assistant.” It’s “a database transaction.” A transaction is a coordination protocol that turns a set of best-effort operations into something you can trust. Multi-agent systems need the equivalent, even when they can’t be fully transactional.

A useful mental model: agents are workers; coordination is management

We get better engineering decisions when we reframe the problem. Agents are workers: specialized, sometimes brilliant, often inconsistent. Coordination is management: the orchestrator, contracts, guardrails, and observability that ensure the organization produces coherent output.

The implication is uncomfortable but liberating: the orchestration layer is the product. Prompts and models are components. Like microservices, the hardest part isn’t writing one service; it’s integration, failure handling, and operational ownership.

In business terms, reliability earns adoption—and budget renewal. If your system can’t handle handoffs, conflicts, and load, it won’t matter how clever the agents are.

Coordination-first multi-agent system architecture (the practical stack)

A production-ready multi-agent system architecture is less about which framework you picked and more about which layers you made explicit. Frameworks come and go; coordination primitives don’t.

The architecture below is a practical stack we’ve seen work across workflow automation: invoice processing, support ticket triage, sales follow-ups, and internal knowledge ops. It assumes LLM agents are tool-using components inside a system with state, contracts, and verification.

The 6 layers: interface → state → tools → agents → coordination → verification

1) Interface: where work enters—API, WhatsApp, web form, email, webhook. If this layer is sloppy, everything downstream inherits ambiguity: missing identifiers, unclear user intent, duplicate requests.

2) State: your source of truth. This is where you store work items, status transitions, artifacts, versioning, and deadlines. If you skip state, you’re building a chatty system that can’t recover after a restart.

3) Tools: the systems you call—CRM, ticketing, payments, docs, ERP. Tool design determines fault tolerance: do you have idempotency keys? do you have consistent error semantics? do you have rate-limit handling?

4) Agents: specialized workers (extract, classify, draft, validate, negotiate). Without clear tool boundaries and artifact schemas, agents will bleed responsibilities and create hard-to-debug coupling.

5) Coordination: the orchestration layer that routes tasks, enforces contracts, manages retries and timeouts, and prevents deadlocks. This is the layer that turns “several agents” into a system.

6) Verification: automated checks that gate writes and finalize outputs. Think: schema validation, policy checks, invariant checks, citations, permissions, and audit logs. Verification is where you convert probabilistic reasoning into deterministic safety.

To make this concrete, map a workflow like invoice processing:

• Interface: invoice arrives via email or upload
• State: create a work item with status NEW → EXTRACTED → VALIDATED → POSTED → CLOSED
• Tools: OCR, accounting API, vendor database, payment approval system
• Agents: extraction agent, validation agent, exception triage agent
• Coordination: orchestrator routes exceptions to human approval after N retries
• Verification: ensure no duplicate payouts, amounts match purchase order, approvals recorded

For tool-calling design and reliability constraints, it’s worth skimming the official docs for the underlying platform you’re using; for example, OpenAI’s documentation is a good reference point for function calling, tool invocation, and response structure.

Centralized coordinator vs decentralized coordination (tradeoffs)

There are two broad ways to do agent orchestration.

Centralized coordinator means one orchestrator owns the state machine. Workers are “dumb” in the best way: they do a task, return an artifact, and don’t mutate the world unless told to. This is simpler to reason about, easier to audit, and often the right choice for enterprise workflows.

Decentralized coordination means agents negotiate, post messages, and decide who does what. This can be more resilient and parallel, but debugging becomes archaeology. Consistency becomes protocol design. Audits become harder, because decision-making is distributed.

Rule of thumb: if you need auditability and deterministic outcomes, start centralized and add decentralization surgically—only where it buys you measurable throughput or resilience.

A simple decision table:

• Compliance-heavy, regulated workflows (refunds, billing, HR actions): start centralized.
• Exploratory research workflows (market research, incident investigation): decentralized can fit, but still needs schemas and ownership rules.

State management: source of truth, idempotency, and ‘who owns the write’

State is where multi-agent systems go from “chatbots” to “systems.” Your biggest win is adopting a single-writer principle for each resource: at any moment, exactly one actor has the right to mutate that object. Everything else is either read-only or submits a proposed change.

In practice, you combine three mechanisms:

• Idempotency keys for tool calls: retries should be safe.
• Versioning (optimistic concurrency): reject stale updates and force a re-read/merge.
• Append-only logs: record decisions and artifacts so you can replay and audit.

A “work item” object is the coordination primitive that makes orchestration deterministic. Here’s a pseudo-JSON schema you can adapt:

WorkItem = { id, type, priority, owner, status, version, created_at, updated_at, deadline, artifacts[], decisions[], tool_receipts[], retries, trace_id }

Notice what’s missing: “chat history.” We’ll get to that.

Designing task handoffs: contracts beat conversations

In multi-agent system development, task handoff is where reliability goes to die. The most common anti-pattern is treating handoffs like a conversation: one agent dumps a paragraph, the next agent interprets it, and everyone hopes the interpretation matches the intent.

Hope is not a coordination protocol. Contracts are.

Handoff contracts: preconditions, postconditions, and acceptance criteria

A handoff contract is a tiny API between agents. It defines what the receiving agent can assume and what it must produce. If you can’t write the contract, you don’t have a stable interface; you have a vibe.

A good contract includes:

• Preconditions: required inputs, required artifacts, tool permissions, and schema versions.
• Postconditions: required output artifacts in structured form (JSON, forms, diffs), not prose.
• Acceptance criteria: validation checks, policy checks, and “done” definitions.

Example: Research Agent → Drafting Agent contract (abridged):

Inputs: topic, audience, required claims; Artifacts: fact_table.json (claims + sources + confidence), outline.json; Outputs: draft.md + citation_map.json; Acceptance: every claim must map to a source URL; no source older than X months unless flagged; draft passes schema and style checks.

The key move is making “done” computable. If your acceptance criteria can’t be checked, it will be argued about—by agents and humans alike.

Avoiding ‘context leaks’: pass artifacts, not chat history

Raw chat transcripts feel safe because they contain everything. In practice, they contain everything and its contradictions. They introduce ambiguity (what’s the latest decision?), drift (what’s authoritative?), and unnecessary tokens (what matters?).

Coordination-first handoffs pass canonical artifacts:

• Summaries with explicit decisions
• Extracted facts with sources
• Structured diffs for edits
• Explicit open questions

Version artifacts so you can reproduce outcomes. When a customer asks “why did the system do that?”, you want an artifact trail, not a 2,000-message transcript.

Before/after, conceptually:

• Before: “Here’s the chat; good luck.”
• After: “Here are artifacts A (facts), B (decision), C (proposed action), each versioned and validated.”

Queues, SLAs, and escalation paths

Handoffs are not just data formats; they’re time commitments. If you don’t design timeouts and escalation, you’ll get infinite loops that look like “the agent is still thinking.”

Use work queues with priority and deadlines. Add routing rules and explicit escalation paths (to a human or supervisor agent). Most importantly: make escalation part of the contract so it’s not an afterthought.

An example SLA table for customer support:

• P1 (service down): first response in 5 minutes; escalation after 10 minutes without progress
• P2 (billing issue): first response in 30 minutes; escalation after 2 retries
• P3 (how-to): first response in 4 hours; escalation only if policy risk detected

This is where you apply classic distributed patterns like retry, backoff, and circuit breaker. Microsoft’s Azure Architecture Center patterns are a concise reference that translates surprisingly well to agent workflow orchestration.

Conflict resolution when agents share resources

The moment two agents can touch the same resource, you’ve introduced conflict. The question isn’t whether conflicts will occur; it’s whether your system treats them as normal, recoverable events or as “surprises” that trigger undefined behavior.

Conflict resolution mechanisms are your safety rails for shared-state automation. They’re also your insurance against expensive, high-trust failures: double refunds, contradictory customer messages, overwritten CRM notes, or compliance violations.

Name the conflict types: write-write, read-write, and goal conflicts

Three conflict types show up repeatedly in LLM agents interacting with tools:

• Write-write conflicts: two agents update the same record (ticket, CRM deal, document) at the same time.
• Read-write conflicts: an agent acts on stale data because the world changed after it read.
• Goal conflicts: one agent optimizes speed or conversion; another optimizes compliance or safety.

A realistic scenario: a sales follow-up agent drafts an email to a regulated customer segment. A compliance agent reviews and edits. Meanwhile, the sales agent re-runs due to a new CRM event and overwrites the compliance edits, because the “latest draft” field is shared and unversioned. You don’t just have bad output; you have an audit problem.

Mechanisms that work: locks, leases, and optimistic concurrency

The coordination-first answer is not “tell the agents to be careful.” It’s applying proven resource management patterns.

• Locks: short critical sections. Use them when a resource update is fast and must be exclusive.
• Leases: time-bound ownership. Use them when an agent needs temporary control but might fail mid-task. Leases prevent permanent deadlocks by expiring.
• Optimistic concurrency: version checks (ETags). If the version changed, reject and retry with merge logic.

Version checks are the simplest and often best default. Example flow: agent reads Ticket(version=7); proposes update; tool call includes “if version==7”; tool rejects because current version is 8; orchestrator re-reads, merges, and retries safely with an idempotency key.

This is also where deadlock prevention matters: never hold locks while calling slow external tools, and prefer leases with explicit deadlines when tasks involve human approval.

Consensus and arbitration: supervisor agent, rules engine, or human-in-the-loop

Not all conflicts should be resolved by “the agent that argues best.” You need an arbitration ladder:

• Deterministic rules (rules engine / policy checks) for clear cases
• Supervisor agent for judgment calls, constrained to approve/reject proposals
• Human-in-the-loop for high-stakes actions (refunds, contract clauses, terminations)

A powerful pattern: a policy agent that cannot execute writes. It only reviews proposed actions against policy and context, then returns approve/reject with reasons. This keeps power concentrated in the orchestration layer, not in the most persuasive agent.

If you want a mental model for rollback and recovery across multiple steps, the distributed-systems world has already named it: the Saga pattern. Martin Fowler’s writing on distributed transactions and sagas is a useful conceptual anchor (see Patterns of Distributed Systems).

Orchestration patterns that scale (without chaos)

Once handoffs and conflicts are explicit, orchestration becomes a design choice, not an emergent property. The trick is choosing coordination patterns that scale without turning your system into an improvisational theater troupe.

Below are patterns that show up repeatedly in multi-agent workflow design with coordination-first architecture. Each can work; each can fail if you ignore state, contracts, and verification.

Coordinator–worker: the default enterprise pattern

If you’re building an enterprise-grade workflow, coordinator–worker is usually the right starting point. The coordinator owns the state machine. Workers are specialized agents: extract, validate, draft, classify, write, notify.

The advantages are practical: traceability, predictable retries, simpler security boundaries, and easier audits. You can also isolate tool access: workers that draft shouldn’t have write access; workers that write shouldn’t generate new intent.

The main risk is coordinator bloat. The fix is keeping the coordinator declarative: routing rules + policy gates + state transitions. Don’t put “clever reasoning” in the coordinator; that belongs in workers producing artifacts.

This is also where business automation becomes measurable. If you’re designing workflows beyond agents—approvals, queues, exception routing—our teams often pair orchestration with workflow process automation with guardrails and measurable outcomes so the system behaves like an operation, not a demo.

Blackboard architecture for shared discovery (and why it needs rules)

Blackboard architecture is a shared workspace where agents post hypotheses, evidence, and intermediate artifacts. Think: incident response, troubleshooting, research. It’s powerful because it enables parallelism and cross-pollination.

It’s also dangerous without rules. Without schemas and ownership constraints, the blackboard becomes cluttered, contradictory, and overwritten. Agents will confidently build on weak or stale facts.

If you use a blackboard, require:

• Typed artifacts with schemas (indicator, hypothesis, decision, action)
• Ownership rules (who can update vs append)
• Verification gates before actions are executed

Marketplace/auction task allocation (useful, but don’t overfit)

Marketplace allocation lets agents “bid” on tasks based on capability, cost, or latency. This is useful when your agents are heterogeneous: different languages, tools, domain knowledge, or rate limits.

It works well for support triage: tasks vary widely, and specialized agents can self-select. But it needs safeguards: prevent gaming, enforce deadlines, and handle no-bid cases (otherwise tasks stall silently).

Use auctions as a scheduling primitive, not as a philosophical stance. In high-audit contexts, you still want a coordinator that owns the final assignment and the state transition.

Deadlock prevention and loop breakers

Deadlocks and loops are the “distributed systems tax” of agent orchestration. In agent terms, deadlock is: agents waiting on each other’s outputs, or a workflow stuck in a non-terminal state because no one owns the next step.

Looping is more insidious: the system makes progress locally (more messages, more retries) but not globally (no state transition, no artifact that resolves uncertainty). You prevent both with:

• Timeouts and attempt counters
• Circuit breakers that trigger escalation
• Progress metrics: every step must change state or produce a new artifact

A common loop: an agent repeatedly asks for missing data from a user who can’t provide it. The fix is a required-fields gate in the interface layer plus an escalation path (“request human support” or “close as incomplete after 2 attempts”).

For graph-based orchestration patterns, LangGraph documentation is a helpful reference. Even if you don’t use LangGraph, the mental model—explicit nodes, edges, and state—pushes you toward coordination-first design.

Testing methodologies for multi-agent coordination reliability

If you test agents like you test chatbots, you’ll ship coordination bugs. The right target of testing is the protocol: the orchestration layer, state transitions, contracts, and invariants.

Coordination testing is where multi-agent system development becomes engineering instead of performance art. It’s also where you earn the right to scale to more agents and more workflows.

Test the protocol, not the prompt: invariants and property-based thinking

Start by defining invariants: things that must always be true regardless of ordering, latency, or retries. Invariants turn vague “correctness” into something you can verify.

Examples of invariants (workflow automation oriented):

• No duplicate payouts for the same invoice ID
• No ticket can be closed without a resolution artifact
• Every work item ends in a terminal state (CLOSED, ESCALATED, FAILED_SAFE)
• Tool writes require verification approval (policy gate passes)
• Every external side effect has a tool-call receipt recorded
• Retries never create new work items unless explicitly de-duplicated
• Version conflicts must lead to re-read + merge, not silent overwrite
• Escalation must happen after N attempts or T time-in-state
• PII never appears in logs beyond allowed fields
• All customer-facing messages have a traceable decision record

Then adopt property-based thinking: randomize ordering, latency, partial failures. The system should still satisfy invariants. Separate model nondeterminism from coordination determinism by mocking tool calls and freezing orchestrator logic. Your goal is deterministic orchestration even if agent text varies.

Fault injection: timeouts, tool failures, stale reads, and partial writes

Production fails in predictable ways: API 500s, rate limits, slow responses, dropped messages, and partial writes. If you don’t test these, you’re not testing reality.

A chaos-style test plan for a 3-agent workflow might inject failures at every boundary:

• Tool call returns 500 → ensure retries with backoff + idempotency
• Tool call times out → ensure timeout triggers re-queue, not duplicate execution
• Stale read → ensure optimistic concurrency rejects write and triggers merge
• Orchestrator restarts mid-flight → ensure state allows resume without orphaned work items

Rollback and recovery is often less about “undo” and more about “compensate.” If step 3 fails after step 2 created a side effect, you need a compensating action or a manual escalation path. Your tests should verify that the system chooses one of those, not neither.

Adversarial and ‘two agents walk into a bar’ scenarios

Concurrency reveals coordination failure modes fast. Deliberately run concurrent requests that target the same resource. Don’t just test “happy parallelism”; test contention.

Example: 20 parallel requests trying to refund the same invoice. Expected outcomes:

• At most one refund executes (idempotency + locks/versions)
• All other attempts fail safe with a clear conflict reason
• The final work item state is consistent and audited

Also test contradictory inputs to force arbitration: customer claims “I was double charged,” finance system shows “single charge,” support notes show “manual adjustment pending.” Your system should follow the arbitration ladder and avoid unsafe tool actions.

Acceptance tests in production: canary runs and shadow mode

Even great staging tests can’t simulate the messy edges of production: real latency distributions, real data weirdness, real human behavior. So you need production acceptance tests that reduce blast radius.

• Shadow mode: generate recommendations, log them, but don’t execute writes.
• Canaries: gradually increase execution percentage, monitor error rates, conflict rates, and recovery metrics.
• Rollback conditions: predefine what triggers disablement (spike in retries, policy failures, or time-in-state).

Do not “flip the switch” on writes without a runbook. Multi-agent system development is an operations problem as much as a build problem.

Observability: the only way to debug coordination at scale

When coordination breaks, your system doesn’t crash neatly. It drifts. It loops. It creates partial progress. It produces outputs that look plausible until they cause damage.

Observability is how you turn that drift into something you can debug. Without agent monitoring and logging, you’ll default to “the model is random,” which is a story that feels explanatory but prevents improvement.

What to log: handoff events, state transitions, and tool-call receipts

Log structured events, not paragraphs. You want: who did what, on which work item, with which version, and why. Every task handoff should be an event with pointers to artifacts. Every tool call should have a receipt.

Sample event schema concepts:

• handoff_completed: { trace_id, work_item_id, from_agent, to_agent, artifacts[], preconditions_met, timestamp }
• tool_call_executed: { trace_id, work_item_id, tool_name, action, idempotency_key, request_hash, response_code, duration_ms }

Be deliberate about PII: log references and hashes where possible, and enforce redaction at the logging boundary. Observability is not an excuse to copy customer data into every system.

Metrics that matter: completion rate, retry depth, conflict rate, and time-in-state

Metrics are your leading indicators of coordination health. The minimal set we recommend before scaling:

• Completion rate (terminal success vs escalation vs failure-safe)
• Retry depth (how many retries per work item; distribution matters)
• Conflict rate (version conflicts, lock contention, arbitration triggers)
• Time-in-state (where work items get stuck)

These map to business outcomes: cycle time, cost per case, and customer satisfaction. A rising conflict rate is not just “technical debt”; it’s a predictor of user-visible weirdness.

Tracing and replay: make failures reproducible

To fix coordination issues, you need reproducibility. Store inputs, tool outputs, and artifact versions so you can replay incidents. Deterministic orchestration means: given the same sequence of events, the orchestrator should produce the same state transitions.

Replay becomes the backbone of postmortems. It shifts the narrative from “AI did something weird” to “our protocol allowed two writers” or “we retried a non-idempotent tool call.” That’s a fixable root cause.

For distributed tracing concepts, OpenTelemetry’s observability primer is a solid foundation. Agents are just another kind of distributed component; the tracing principles apply directly.

When to hire multi-agent system development services (and what to ask)

There’s a moment in every serious project where “we can hack this together” becomes “we need to own this in production.” That’s often when teams consider multi-agent system development services or an enterprise multi-agent system development company.

The right question isn’t “can you build agents?” It’s “can you build coordination?” Because that’s where the risk lives.

Build vs buy vs partner: a decision rubric

Build, buy, or partner depends on workflow stakes, integration complexity, and how much operational ownership you’re prepared to take on.

• Build in-house if you have a strong platform team, clear workflows, and tolerance for iteration.
• Buy if the use case is narrow and integration is minimal (and you can accept vendor constraints).
• Partner if the workflow is high-stakes, compliance-heavy, spans multiple systems of record, and you need speed without operational shortcuts.

A simple rubric (use this in a steering meeting):

• Risk of incorrect writes (low/medium/high)
• Number of integrations and systems of record
• Auditability requirements
• Need for human-in-the-loop
• Internal bandwidth (platform + ops)
• Expected concurrency/load variability
• Data sensitivity and compliance constraints
• Time-to-value expectations

Vendor questions that expose coordination maturity

If you’re evaluating multi-agent system development services, ask questions that force concrete artifacts, not slides. Here’s a copy/paste checklist for an RFP or vendor call:

• Show us your work-item state machine for a similar workflow.
• How do you implement task handoff contracts (schemas, acceptance checks)?
• What’s your idempotency strategy for tool calls?
• How do you handle write-write conflicts (locks, leases, optimistic concurrency)?
• What are your default timeout and retry policies? Where are circuit breakers?
• How do you prevent deadlocks and infinite loops?
• What invariants do you test? Do you run concurrency tests?
• What observability do we get (event schema, trace IDs, dashboards)?
• How do you do safe rollout (shadow mode, canaries)?
• Who owns incident response and ongoing model/tool updates?

Good vendors will show you artifacts. Great vendors will show you incidents they’ve learned from and how the protocol changed.

How Buzzi.ai builds coordination-first multi-agent systems

At Buzzi.ai, we treat multi-agent system development as a full-stack reliability problem: agents + orchestration + verification + operations as one deliverable. That’s how you ship workflows that survive real-world contention, partial failures, and messy inputs.

Our process is workflow-first: we start with the work-item state machine, define task handoff contracts, and implement verification gates before scaling the number of agents. We also design for operational constraints that matter in emerging markets—latency, noisy inputs, and WhatsApp-first interfaces—where reliability is non-negotiable.

If you want a team to design and build coordinated agent systems (not just a demo), start here: AI agent development services for coordinated multi-agent workflows.

Conclusion

Multi-agent system development succeeds when coordination is treated as core architecture, not glue. Handoffs need contracts and artifacts, because transcripts create ambiguity and drift. Conflict resolution needs explicit ownership, idempotency, and arbitration paths, because shared resources guarantee contention.

Reliability comes from coordination-focused tests: invariants, fault injection, and concurrency scenarios. Observability—events, metrics, and replay—turns “AI randomness” into debuggable engineering.

If you’re moving from a multi-agent demo to a production workflow, start with a coordination review: define your work-item state machine, handoff contracts, and failure tests before you add more agents.

If you’d like help turning your agentic prototype into an operational system, we can design the orchestration layer, verification gates, and rollout plan with you. Explore our AI agent development services and reach out when you’re ready.

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