AI INTEGRATION
Multi-agent orchestration services for when one agent is no longer the problem. Planners, task queues, handoffs, retries, shared state and durable long-running work - the layer that makes several specialists behave like one team, with a single audit trail and one place to look when it stalls.
What orchestration does that agents cannot do alone
Multi-agent orchestration is the layer that decomposes a goal into tasks, assigns each to the agent best suited to it, sequences the dependencies, handles failures, and holds the state of work that runs for hours or days. It is the difference between a set of agents and a system.
Every agent added to a process multiplies the number of ways it can go wrong. Two agents can disagree about the same record. A handoff can lose context. A retry can duplicate an action that already succeeded. Work that takes three days has to survive a deployment, a rate limit and a restart. None of these are model problems, and none of them are solved by a better prompt.
The design goal is boring and specific: at any moment you should be able to answer what is running, what it is waiting on, what failed, and what it will do next. Orchestration that cannot answer those four questions is a distributed system nobody can operate.
You probably need this if you recognize these
- You have several agents and no single view of what is running.
- Two agents have written conflicting values to the same record.
- A failure halfway through leaves work in a state nobody designed.
- Long-running jobs quietly die and nobody notices for a day.
- Retries have caused a duplicate action at least once.
- Adding an agent now feels risky rather than routine.
The orchestration layer, component by component
A planner
Decomposes a goal into tasks and dependencies, using deterministic plans where the process is known and model-driven planning only where it genuinely varies.
A task queue
Durable, prioritized, with visibility timeouts, so nothing is lost when a worker dies mid-task.
Handoff contracts
A defined payload between agents, so context is passed explicitly rather than assumed to be shared.
Idempotency and retries
Every task safely repeatable, with backoff and a dead-letter path for what will never succeed.
Shared state
One record of truth for a run, with conflict handling where two agents touch the same entity.
Durable execution
Long-running work that survives restarts and deployments, resuming rather than restarting.
Escalation
A defined route when the system is stuck, rather than a job that retries forever.
One audit trail
A single trace across every agent, task and system call in a run.
How a goal becomes coordinated work
Multi-agent orchestration services
Plan
The goal is decomposed into tasks with explicit dependencies and success criteria.
Self-healed - retried with fallback tool. Human not required.
Dispatch
Each task goes to the agent suited to it, with the context it needs handed over explicitly.
Track
The run's state is durable, so progress survives a restart or a deployment.
Handle failure
Failures retry with backoff, reroute, or go to the dead-letter path with the reason recorded.
Reconcile
Conflicting writes are detected and resolved by rule, not by whichever agent finished last.
Close or escalate
The run completes with a full trace, or stops and escalates with what it was waiting on.
Agents that only work in coordination
Incident Response Agent
Coordinates diagnosis, communication and remediation across several specialists under time pressure.
Build & Release Agent
Sequences dependent steps where a failure halfway through must roll back cleanly.
Email Campaign Agent
Long-running, scheduled work that must survive restarts without double-sending.
What orchestration sits across
Orchestration coordinates agents. Reaching your systems is [AI integration](/services/ai-integration-services/), and the two are scoped separately for a reason.
- Queues and message brokers
- Workflow and durable execution engines
- Observability and tracing
- Your agents' target systems
- Alerting and on-call
- Data warehouse, for run analytics
- Identity, for per-agent credentials
Platform names are shown as examples of the categories agents connect to. They are not partnerships or endorsements.
Keeping a multi-agent system controllable
Autonomy boundary
Set per agent and per run. A run has a budget in tasks, time and cost, and it stops when it reaches it rather than looping.
Approval gates
Irreversible steps pause the run and wait for a named approver, with the run’s state held durably meanwhile.
What stays human
Deciding what a stuck run should do, and any conflict the reconciliation rules cannot resolve.
Logging
One trace per run covering every task, agent, model call and system call, which is the only practical way to debug emergent behavior.
How the work runs
Typical ranges from our engagement model (doc 04 §5), not a quote.
| Stage | Typical | What happens |
|---|---|---|
| Pilot | 3-10 days audit, then 2-4 weeks | The process is modeled as tasks and dependencies, and one multi-agent path is proven end to end including its failure modes. |
| Build | 3-8 weeks | Planner, queue, handoff contracts, state, retries, durable execution, escalation and tracing. |
| Release | 1-2 weeks | Run in parallel with the existing path, compared run by run before it takes over. |
| Managed | ongoing, optional | New agents added to the plan, budgets tuned, stuck-run patterns turned into rules. |
What we measure
- One trace
- Every run reconstructable end to end across all agents (Target)
- Idempotent
- Every task safely repeatable, so a retry cannot double-act (Target)
- Bounded
- Every run has a task, time and cost budget it cannot exceed (Target)
- Your baseline
- Stuck-run rate and end-to-end time, measured before and after (Yours)
“ Until then these are design targets and engineering commitments, not results.
What this looks like in practice
Logistics exception hive
Reference scenario · Logistics. Shipment exceptions detected and actioned across several coordinating agents.
Reference scenario - a composite build illustrating our method. Figures are modeled and the model is shown.
Frequently asked questions
Do we need orchestration, or just better agents?
If one agent finishes your process, you do not need this. You need it when a process spans several specialists, when work runs long enough to outlive a deployment, or when a failure halfway through leaves things in a state nobody designed. The signal is usually that adding an agent now feels risky rather than routine.
Should agents plan dynamically or follow a fixed workflow?
Mostly fixed, in our experience, and we are deliberately conservative here. Deterministic plans are cheaper, faster, testable and far easier to debug. Model-driven planning earns its place where the path genuinely varies per case. Systems that let agents plan everything freely tend to be impressive in a demo and unpredictable in production, which is the wrong trade for a business process.
How do you stop agents from conflicting with each other?
Explicit handoff contracts, one durable record of truth per run, and reconciliation rules for the cases where two agents legitimately touch the same entity. Conflicts are detected and resolved by rule rather than by whichever agent wrote last, and every write is attributed so a wrong value can be traced.
What happens if a run gets stuck?
It stops. Every run has a budget in tasks, time and cost, and reaching it triggers escalation with the run's state intact, showing what it was waiting on. Runs that retry indefinitely are a cost problem and an operational one, so they are designed out rather than monitored for.
Which orchestration framework do you use?
We choose per engagement and are not committed to one, because this part of the ecosystem is moving quickly and today's best choice is unlikely to hold for three years. What we hold constant are the properties: durable state, idempotent tasks, explicit handoffs, bounded runs and one trace. Frameworks that cannot provide those are ruled out regardless of popularity.
Can you orchestrate agents we already have?
Usually. We assess what exists, wrap them in handoff contracts and put them behind a durable queue and one trace. Agents that are not idempotent typically need work before they can be safely retried, and that is scoped honestly rather than absorbed.
Related services
Data Engineering for AI
The data these runs read and write.
Self-Healing Infrastructure Agents
Coordination under incident conditions.
Managed AI Workforce
Running the orchestrated hive once it is live.