AI Agent Orchestration: Definition, Patterns, and Real Examples

Definition (how we use it): AI agent orchestration is the control layer that routes work between agents and tools, manages shared context, and enforces guardrails so multi-step automation stays safe and auditable.

A single agent can be useful: it reads a ticket, fetches a record, drafts a reply. Orchestration becomes necessary when you need coordination across steps, systems, or risk levels. In our work, the moment a workflow involves more than one tool (helpdesk + order system + email, or bank feed + receipts + VAT overview), we treat orchestration as part of the product—not an afterthought.

A useful way to think about it:

• Agents are specialists (triage, extraction, calculation, execution).


• Tools are where work happens (APIs, databases, workflow engines).


• The orchestrator decides who does what, in what order, with what permissions, and what happens when something goes wrong.

What an orchestrator usually contains

In real deployments, orchestration shows up as a few concrete components:

• A tool registry (what tools exist, how to call them, and what each call is allowed to do)


• A policy layer (business rules + risk tiers + approval requirements)


• A state store (case ID, extracted fields, confidence, decision history)


• A human review queue (the explicit handoff step, not a vague “escalate”)


• An evaluator (metrics, sampling, and regression tests)

A practical way to spot when orchestration is needed: if you find yourself adding “just one more tool” and “just one more exception rule” every week, you’re already doing orchestration—just without a clean design.

Multi-agent systems are not magic. They’re a decomposition strategy. Instead of asking one model to do everything, you split the work into roles with clear responsibilities and interfaces.

In a production system, a multi-agent architecture typically includes:

• Specialised agents: each agent has a narrow goal (classify ticket, extract invoice fields, compute VAT, execute refund).


• Message passing: agents communicate via structured messages (JSON payloads, events, or tasks), not long chat transcripts.


• Shared memory/state: a store for the case context (ticket ID, customer ID, current status, extracted fields, confidence).


• A coordinator/supervisor: the orchestrator that assigns tasks, merges results, and decides escalation.

1) Minimal context fetch: pull only the fields needed for the next decision (privacy + speed).
2) Idempotent actions: tool calls should be safe to retry (important when APIs time out).
3) Explicit risk tiers: read-only, reversible writes, irreversible writes.
4) Verification: re-fetch after writes and check invariants (did the status actually change?).

Orchestration is also where humans fit. We don’t treat “human-in-the-loop” as a handoff to a random inbox. We treat it as a deliberate step with a clear payload: the agent proposes an action, shows evidence, and a human approves or edits.

Message design (why structure beats chat transcripts)

When agents pass work to each other, we prefer structured payloads over raw conversation. A typical task message contains:

• case_id (so everything ties back to one workflow instance)


• task and success_criteria


• inputs (IDs and minimal fields)


• tools_allowed (explicit permissions for this step)


• risk_tier (read-only vs reversible write vs approval required)

Conflict resolution is the other hard problem. In a parallel setup, two agents can disagree (for example: one classifies a vendor as “travel”, another as “office expense”). The orchestrator needs a rule: choose the higher-confidence result, route to a human, or run a verification tool call.

Shared memory and state (what we store, and what we don’t)

In multi-agent orchestration, “memory” is mostly system state, not a mystical brain. We typically store: case status, extracted fields, confidence scores, and links to source records. We avoid storing full personal data in multiple places; instead we store IDs and fetch details on demand.

There are a few common state patterns:

• Event log: append-only actions with timestamps (good for audit trails).


• Snapshot state: the current view of the case (good for fast UI).


• Embeddings/vector memory: useful for retrieval, but not a substitute for a system of record.

In practice, we reuse a small set of orchestration patterns. The right choice depends on latency, risk, and how many tools are involved.

1) Sequential (pipeline)

Text diagram: Input → Extract → Decide → Execute → Verify → Log

Use this when steps depend on each other (you can’t calculate VAT before you extracted the VAT breakdown). It’s predictable and easy to audit.

2) Parallel (fan-out + merge)

Text diagram: Input → (Agent A + Agent B + Agent C) → Merge

Use this when you can fetch context in parallel: order status, customer tier, and past tickets. It reduces latency but requires conflict resolution (“which source is the truth?”).

3) Hierarchical (supervisor + workers)

Text diagram: Supervisor → delegates tasks → workers return results → supervisor decides

Use this when you need a policy brain. A supervisor agent can enforce the business rules while worker agents do extraction or execution. This pattern is common in regulated workflows because it centralises control.

4) Reactive (event-driven)

Text diagram: Event bus → triggers agents based on event type

Use this when work is triggered by events: new ticket, new bank transaction, new invoice, payment received. It scales well and fits modern system design.

What we avoid: “One mega-agent does everything.” It’s harder to test, harder to secure, and harder to reason about when it fails. Orchestration gives you seams: you can measure and improve each step.

More patterns we use in practice

5) Plan → execute → verify loop

Text diagram: Plan (structured) → Execute (tools) → Verify (re-fetch + invariants)

This pattern is the backbone of safe automation. The verification step is where you prevent silent failures.

6) Voting / consensus for risky decisions

In high-impact areas, we sometimes run two independent classification passes (different prompts or different models) and require agreement before executing. If they disagree, it routes to review.

7) Human gate as a first-class step

Instead of “escalate”, define the gate: what the human sees (summary + evidence), what actions they can approve, and what gets logged. This reduces handoff time and keeps accountability clear.

Latency note: parallelism is not free. It reduces wall-clock time, but increases tool calls and complexity. We only parallelise when it meaningfully improves user experience (for example: fetching order status and customer tier simultaneously).

Compensation and circuit breakers (boring but essential)

Once agents can call tools, you need the same reliability patterns you’d use in any distributed system:

• Circuit breakers: if an API is failing, stop calling it and route to fallback.


• Rate limits: protect your own systems from bursts when you parallelise.


• Compensation steps: if you created something by mistake, define the reversal (cancel label, revert status, undo booking).

Bookkeeping is a great orchestration example because the workflow is multi-step and the cost of mistakes is real. Here’s a simplified version of how we think about orchestration for Fiscal Agent.

Pipeline:

1) Transaction import agent: imports bank transactions via PSD2 (or CSV) and normalises descriptions.
2) Categorisation agent: applies vendor patterns and proposes a category with confidence.
3) Receipt/OCR agent: checks for evidence, extracts VAT breakdown when available, and flags missing receipts.
4) VAT calculation agent: applies Dutch VAT logic (21% standard, 9% reduced, 0% for specific cases) and updates quarter-to-date totals.
5) Anomaly agent: flags duplicates, unusual amounts, or patterns that don’t match history.
6) Reporting agent: produces a VAT overview and an exception list for review before the quarterly deadline (30 April, 31 July, 31 October, 31 January for quarterly filers).

Orchestration matters because each step has different risk. Importing is low-risk. Categorisation is medium-risk (it can be corrected). Submitting a VAT return is high-risk (and still requires human submission).

The orchestrator enforces guardrails: if evidence is missing, the booking is parked; if confidence is low, it is routed to review; if KOR eligibility is relevant (€20,000 threshold), the system tracks turnover and surfaces warnings early. That’s orchestration: not “more AI”, but controlled workflow execution.

VAT split example (why orchestration prevents wrong bookings)

A common Dutch nuance is mixed VAT on one receipt (for example: supermarket purchases can include both 9% and 21%). A chatbot can tell you the rates. An orchestrated bookkeeping system can do the work: detect missing evidence, extract the VAT breakdown from the receipt, and only then update VAT totals.

Example numbers: a receipt shows €30.00 at 9% and €24.30 at 21%. Deductible input VAT is €2.48 + €4.22 = €6.70. If you book the whole thing as 21%, your VAT reclaim is wrong.

Orchestration also supports retention and auditability. We store the evidence link and the decision trail so you can answer questions later. In the Netherlands, administrative records are generally kept for 7 years (and 10 years for certain real-estate-related records).

Orchestration improves when tooling becomes standardised. Two protocol efforts are worth understanding because they aim to make tool access and agent coordination more consistent.

Model Context Protocol (MCP)

MCP (introduced publicly in late 2024) standardises how an AI system connects to tools and context providers. In practice, it’s a way to describe: what tools exist, how to call them, what data they return, and what permissions apply. For orchestration, this reduces glue code and makes tool access more auditable.

Agent2Agent (A2A)

Agent2Agent (publicly discussed around April 2025) focuses on standardising how agents talk to each other: message formats, task handoffs, and identity. For multi-agent systems, this helps you avoid “custom protocol per project.”

We care about these protocols for a pragmatic reason: Dutch businesses need integrations that survive vendor changes. When tool access and agent messages are explicit and standardised, it’s easier to test, monitor, and explain.

What MCP changes in practice

MCP is useful because it makes tool access explicit. Instead of hiding integrations inside custom code, you describe them as callable tools/resources with clear schemas. That improves:

• Discoverability (the agent knows what tools exist)


• Testing (you can mock tool calls)


• Governance (you can review what data flows where)

A2A-style protocols aim to make handoffs predictable: a task is created, an agent accepts it, and results come back in a known format. For orchestration, that reduces the number of one-off adapters you need when you add a new agent role.

We treat both as part of the same direction: standardise interfaces so your automation is portable. That matters for Dutch businesses because vendor lock-in and unclear data flows are the two most common reasons AI projects get blocked by security and compliance teams.

MCP servers, clients, and transports

Practically, MCP introduces a server concept: a tool provider exposes capabilities (tools/resources/prompts), and a client (your agent runtime) connects to it. Different transports exist (for example: local stdio or HTTP-style connections), but the important part is the same: the interface is explicit and inspectable.

For orchestration, that means you can treat tools as modular building blocks instead of rewriting integration glue per agent.

You probably don’t need multi-agent orchestration on day one. Here’s when it becomes worth it.

Start with a single agent when:

• The workflow is one tool deep (read ticket → draft reply)


• Risk is low and outputs are reversible


• You’re still learning what the exceptions look like

• The workflow spans multiple systems (helpdesk + ERP + email)


• You need different risk tiers and approvals


• You want observability per step (where does it fail?)


• You want to run steps in parallel to reduce latency

Complexity and risk heuristic

We use a simple heuristic: orchestration is worth it when either complexity or risk is high.

• Complexity is high when you have 6+ steps, multiple tools, or many exceptions.


• Risk is high when the workflow touches money, statutory filings, or sensitive personal data.

Typical orchestration triggers we see

Orchestration usually becomes necessary after the first pilot, when you run into reality:

• You need to fetch context from two systems and reconcile differences.


• You need a verification step because “success” is not just sending a message—it’s updating the right record.


• You need different approval rules per customer tier, product type, or financial amount.


• You need a clean audit trail because someone will ask “why did we do this?” months later.