Multi-Agent AI Systems: Orchestrating Specialised Agents for Enterprise Workflows
Gartner does not report a 1,445% increase in enquiries about a technology that is merely interesting. That figure — the surge in client questions about multi-agent AI systems recorded in 2025 — reflects something more significant: a recognition among enterprise architecture and technology leadership that the fundamental unit of agentic AI is shifting. Single-purpose agents that execute one workflow in isolation are giving way to orchestrated systems in which teams of specialised agents collaborate, hand off work between them, and collectively execute processes of a complexity that no single agent could reliably handle.
This article is not an introduction to AI agents — we covered that ground in our guide to building AI agents for enterprise automation. Nor does it cover Microsoft-specific agent development, which we addressed in our MCP and Microsoft 365 Copilot agents guide. This article addresses the specific architectural challenge of multi-agent orchestration: how you design a system in which multiple specialised agents operate as a coherent whole, how you manage state and communication across agent boundaries, and how you build in the governance controls that make such systems trustworthy enough to deploy in production enterprise environments.
Why Single-Agent Architectures Break Down at Scale
To understand the case for multi-agent AI systems enterprise deployments, it helps to be precise about where single-agent architectures fail.
A single agent operates with one reasoning context: a system prompt that defines its purpose, a tool set that defines its capabilities, and a context window in which all observations, reasoning, and intermediate results accumulate. As the workflow grows more complex, three problems emerge.
Context saturation. LLM context windows are finite. For a simple five-step workflow, a single agent’s context remains manageable. For a twenty-step enterprise process — one involving dozens of tool calls, several external data lookups, and multiple decision points — the accumulated context approaches or exceeds the model’s practical reasoning limits. Performance degrades non-linearly; the model’s ability to maintain coherent reasoning across a long context is not a linear function of context length.
Tool set proliferation. A single agent handling an end-to-end enterprise workflow requires access to every tool used at any point in the process. A procurement workflow alone might touch an ERP system, a supplier database, a compliance checking API, a document generation service, a spend analytics platform, and an approval workflow system. Presenting an agent with thirty tools and instructing it to select the right one at each step dramatically increases the probability of incorrect tool selection compared to presenting a specialist agent with five tools precisely scoped to its domain.
Domain expertise dilution. A system prompt that tries to encode the knowledge and decision logic for multiple distinct domains — financial analysis, legal compliance, technical evaluation, supplier relationship management — produces an agent that is mediocre at each rather than expert at any. Specialisation is as valuable in AI agent design as it is in human organisational design.
Multi-agent systems address all three failure modes by decomposing complex workflows across agents with narrower, more focused responsibilities.
The Four Core Orchestration Patterns
AI agent orchestration patterns architecture can be categorised into four principal designs, each suited to different workflow characteristics. Real enterprise deployments often combine these patterns; understanding them individually is the prerequisite for composing them effectively.
Supervisor Pattern
In the supervisor pattern, a central orchestrator agent receives the top-level task, decomposes it into subtasks, assigns each subtask to an appropriate specialist agent, receives the results, and synthesises a final response. The orchestrator maintains the overall task state and decides at each step which specialist agent to invoke next.
User Request
│
▼
┌─────────────────┐
│ Supervisor │ ← Holds task state, routes subtasks
│ Orchestrator │
└────────┬────────┘
│ delegates subtasks
┌────┴─────────────────────┐
▼ ▼ ▼
┌────────┐ ┌────────┐ ┌──────────┐
│ Agent A│ │ Agent B│ │ Agent C │
│(Domain1│ │(Domain2│ │(Domain 3)│
└────────┘ └────────┘ └──────────┘
│ │ │
└───────────┴──────────────┘
│ results
▼
Final synthesis
The supervisor pattern is the most general-purpose orchestration architecture and the appropriate default for workflows with variable structure — where the sequence and combination of specialist agents depends on what is discovered at runtime rather than being predetermined. The orchestrator must be implemented with a powerful, capable model; it carries the full reasoning burden of task decomposition and synthesis. Specialist agents can often use smaller, faster, cheaper models precisely because their task is narrowly defined.
A critical implementation detail: the supervisor agent must be designed with explicit awareness of its routing responsibilities. Its system prompt should specify the available specialist agents, their capabilities and limitations, and the criteria for task delegation. Ambiguity in the orchestrator’s routing logic is a primary source of failure in supervisor pattern implementations.
Pipeline Pattern
In the pipeline pattern, workflow stages are arranged as a directed sequence. Each agent receives the output of the previous agent as its input, enriches or transforms it, and passes the result to the next agent. There is no central orchestrator; the pipeline is managed by the infrastructure rather than by an agent.
Input
│
▼
┌──────────┐
│ Stage 1 │ (Classification Agent)
│ Agent │
└────┬─────┘
│
▼
┌──────────┐
│ Stage 2 │ (Extraction Agent)
│ Agent │
└────┬─────┘
│
▼
┌──────────┐
│ Stage 3 │ (Validation Agent)
│ Agent │
└────┬─────┘
│
▼
┌──────────┐
│ Stage 4 │ (Routing Agent)
│ Agent │
└──────────┘
│
▼
Output
The pipeline pattern is optimal for workflows with fixed, sequential structure — where the steps are always the same and the output of each step is a well-defined input to the next. Document processing is the canonical enterprise use case: every incoming document passes through the same classification, extraction, validation, and routing stages in sequence.
Pipeline architectures are simpler to reason about, debug, and monitor than supervisor-based systems because the control flow is deterministic. They are also easier to test: each stage can be evaluated independently with defined input and output schemas. The trade-off is inflexibility — pipelines do not handle conditional branching or dynamic task composition elegantly. For workflows that are mostly sequential but occasionally require conditional routing, a hybrid approach that embeds a lightweight supervisor at the branching point within an otherwise pipeline architecture is often the right solution.
Consensus Pattern
In the consensus pattern, a task is submitted to multiple independent agents simultaneously. Each agent produces a response using its own reasoning. A synthesis layer then aggregates the responses — through voting, weighted combination, or a dedicated arbitration agent — to produce a final output.
Task
│
┌─────┴──────┐
│ Parallel │ (fan-out)
│ Dispatch │
└──┬───┬───┬─┘
│ │ │
▼ ▼ ▼
┌──┐ ┌──┐ ┌──┐
│A1│ │A2│ │A3│ ← Independent agents (same or different models)
└──┘ └──┘ └──┘
│ │ │
┌──┴───┴───┴─┐
│ Synthesis │ (vote / arbitration / confidence-weighted merge)
└─────────────┘
│
Output
The consensus pattern is appropriate for high-stakes decisions where individual agent errors carry significant consequences, and where the cost of multiple parallel LLM calls is justified by the risk reduction. Common enterprise applications include legal document review, financial compliance checking, and security vulnerability analysis — scenarios where a single agent’s hallucination or reasoning error could have material consequences.
Consensus is not majority voting in the naive sense. In practice, agents in a consensus system are often given different system prompts, different context, or different model versions to maximise independence. A synthesis agent (or rule-based arbitration logic) then evaluates the degree of agreement, surface disagreements for human review when consensus is not reached, and applies confidence-weighted aggregation when responses partially overlap.
The governance implication of the consensus pattern is positive: disagreement between agents is itself a meaningful signal. A consensus system that surfaces inter-agent disagreements to human reviewers is more auditable and more aligned with human oversight requirements than a single-agent system that produces a confident but potentially wrong answer.
Hierarchical Pattern
The hierarchical pattern nests orchestration across multiple levels. A top-level orchestrator manages a set of sub-orchestrators, each of which manages its own team of specialist agents. This pattern is appropriate for enterprise deployments of significant scope — multi-department workflows, cross-system integrations, or agentic AI enterprise deployment strategy that spans multiple business units.
┌──────────────────────────┐
│ Enterprise Orchestrator │
│ (Top-level) │
└────────────┬─────────────┘
│
┌─────────┴───────────┐
│ │
▼ ▼
┌────────────┐ ┌────────────┐
│ Sub-orch │ │ Sub-orch │
│ (Procure.) │ │(Compliance)│
└──────┬─────┘ └──────┬─────┘
│ │
┌────┴────┐ ┌────┴────┐
│ │ │ │ │ │ │ │
▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼
Sp Sp Sp Sp Sp Sp Sp Sp
(specialist agents) (specialist agents)
The hierarchical pattern enables very large multi-agent systems to be built without the central orchestrator becoming a bottleneck or a single point of failure. Sub-orchestrators handle the detailed coordination within their domain; the top-level orchestrator concerns itself only with cross-domain coordination and overall task progress.
The principal challenge of hierarchical architectures is state propagation. When a specialist agent at level three of the hierarchy surfaces an error or ambiguity, the information must propagate up through sub-orchestrators to the level at which it can be resolved and then cascade back down with instructions. This requires explicit design of the inter-level communication protocol — what information is passed upward, at what granularity, and how responses propagate downward.
Inter-Agent Communication and State Management
The mechanics of how agents communicate and how state is shared across an agent network are engineering decisions with significant consequences for reliability, debuggability, and governance.
Structured Message Protocols
Ad hoc, free-form communication between agents — one agent passing a natural language summary to the next — is tempting to implement quickly but creates serious problems at scale. When an agent fails or produces an unexpected output, diagnosing the cause requires understanding what it received, and free-form messages are difficult to validate programmatically.
Production multi-agent AI systems enterprise deployments should define structured message schemas for inter-agent communication. Each agent publishes outputs in a defined format: typed fields, validated values, explicit status codes. Downstream agents consume these structured messages and can validate them before proceeding. This makes failures explicit — a downstream agent that receives an invalid message can surface a typed error rather than silently propagating a malformed state.
Shared State vs Message Passing
Two fundamental approaches to state management exist in multi-agent systems: shared state (a common data store accessible to all agents) and message passing (agents communicate exclusively through messages, with no shared mutable state).
Shared state is simpler to implement for workflows that require frequent cross-agent reads of common data — a procurement workflow in which multiple agents need to read the current purchase order state, for example. The risk is contention and consistency issues: when multiple agents can write to shared state, careful concurrency control is required.
Message passing is architecturally cleaner and more aligned with event-driven enterprise architectures. Each agent operates on the messages it receives, produces output messages, and does not directly modify state held by other agents. This simplifies reasoning about the system but requires careful design of the message routing logic and can introduce latency in workflows that require many sequential cross-agent interactions.
In practice, a hybrid approach is common: a shared read-only knowledge base (reference data, configuration, business rules) combined with message passing for workflow coordination.
Conversation History and Context Boundaries
Each agent in a multi-agent system maintains its own conversation history. A critical architectural decision is what portion of the overall workflow history each agent receives. Passing the entire history of all previous agent interactions to each new agent is tempting but counter-productive: it inflates context unnecessarily, increases latency and cost, and can confuse specialist agents with information outside their domain.
Best practice is to pass each specialist agent only the context relevant to its task: the specific input it needs to process, the relevant subset of prior decisions that affect its work, and any constraints or parameters established by the orchestrator. The orchestrator maintains the authoritative record of overall workflow state; specialist agents receive a curated slice.
Error Handling and Resilience
A multi-agent system introduces multiple potential failure points compared to a single agent. Each agent can fail independently, and failures can propagate through the system in ways that are difficult to anticipate. Designing for resilience is not optional in production enterprise deployments.
Agent-Level Failure Modes
Individual agents fail in several distinct ways, each requiring a different response:
Hard failures — the agent is unable to produce any output due to a model API error, a tool failure, or an unhandled exception. These should be caught at the orchestration layer and trigger an explicit retry or fallback strategy.
Soft failures — the agent produces output, but with low confidence or with explicit uncertainty indicators. The system should route low-confidence outputs to human review rather than passing them downstream as authoritative.
Silent failures — the agent produces output that appears structurally valid but is semantically incorrect. These are the hardest to detect and the most dangerous. Mitigation requires downstream validation agents, cross-agent consistency checks, and sampling-based human review of agent outputs in production.
Fallback Strategies
Every specialist agent in a production multi-agent system should have a defined fallback strategy:
- Retry with backoff for transient API failures, with a maximum retry count and a dead-letter mechanism for tasks that exhaust retries.
- Alternative agent routing when a specialist agent is unavailable — routing to a generalist agent or a different specialist capable of partially fulfilling the task.
- Human escalation as the fallback of last resort — surfacing the task to a human operator with full context of what the agent attempted and where it failed.
- Graceful degradation for non-critical path agents — allowing the workflow to continue with reduced functionality rather than halting entirely when a supplementary agent fails.
Circuit Breakers
When a specialist agent begins failing repeatedly, continuing to route work to it degrades overall system performance. Implement circuit breaker logic at the orchestration layer: after a defined failure threshold, the circuit opens and the orchestrator routes around the failing agent, notifying operations teams and activating the fallback strategy. The circuit can be tested periodically (half-open state) and closed again once the underlying failure is resolved.
Practical Example: Document Processing Pipeline
A document processing pipeline is the canonical multi-agent AI systems enterprise use case. The following describes a production-grade architecture combining the pipeline and supervisor patterns.
An enterprise receives thousands of inbound documents daily across multiple channels — email attachments, customer portal uploads, EDI feeds, and scanned post. Each document must be classified, data extracted, the extracted data validated against business rules, and the document routed to the appropriate downstream workflow.
Stage 1 — Ingestion and pre-processing agent. Receives raw documents from all input channels, normalises format (OCR for scanned documents, text extraction from PDFs and Office files), and produces a structured document record with metadata (source channel, received timestamp, file type, document identifier) alongside the extracted text. This agent uses no LLM reasoning — it is a deterministic processing stage.
Stage 2 — Classification agent. Receives the pre-processed document record and classifies the document type (invoice, purchase order, delivery note, contract amendment, customer complaint, regulatory filing). Outputs a structured classification result with a document type, a confidence score, and a list of candidate classifications when confidence is below the threshold. For documents above the confidence threshold, the pipeline continues automatically. For documents below the threshold, the classification result is routed to a human review queue.
Stage 3 — Extraction agent. Receives the classified document and applies domain-specific extraction logic for the document type. An invoice extraction agent knows to extract supplier name, invoice number, line items, VAT amounts, and payment terms. A contract amendment extraction agent extracts clause references, amendment text, effective dates, and signatory requirements. The extraction agent produces a structured data object conforming to the schema for the document type.
Stage 4 — Validation agent. Receives the extracted data and validates it against business rules: does the supplier exist in the supplier master? Does the invoice amount match the purchase order within tolerance? Are all mandatory fields present? Does the contract amendment reference a valid existing contract? The validation agent produces a validation result with a pass/fail status, a list of validation failures with their business rule references, and recommendations for each failure (auto-correct, request clarification, escalate to human).
Stage 5 — Routing agent. Receives the validated document record and determines the downstream workflow: invoice to accounts payable processing, delivery note to goods receipt matching, complaint to customer service CRM, contract amendment to legal review queue. The routing agent applies business rules that may be complex — routing based on supplier tier, document value, business unit, and exception flags from the validation stage.
This pipeline processes routine documents autonomously. The supervisor pattern activates when documents fail validation or classification: a supervisor agent receives the exception, assesses the appropriate human escalation path, assembles the context package (original document, extracted data, validation failures, confidence scores), and routes to the appropriate human reviewer with a structured task.
Practical Example: Procurement Multi-Agent Workflow
A procurement workflow illustrates the supervisor pattern operating across a more complex, non-linear process. An agentic AI enterprise deployment strategy for procurement must handle the inherent variability of commercial negotiations, supplier availability, and compliance requirements.
The top-level procurement orchestrator receives a validated purchase requisition and manages a team of four specialist agents:
Specification matching agent. Analyses the requisition specification and matches it against the organisation’s approved product catalogue and historical purchase data. Identifies exact matches, close matches, and gaps where no approved product exists. Returns a ranked list of catalogue matches with match confidence, compliance status (approved supplier, preferred supplier, or requires procurement approval), and historical pricing data.
Supplier evaluation agent. For requisitions that require sourcing beyond the approved catalogue, queries the supplier database, evaluates supplier qualifications (financial stability, quality certifications, geographic coverage, delivery lead times), and produces a shortlist of qualified suppliers with a structured evaluation matrix. For high-value requisitions above the threshold defined in the orchestrator’s configuration, the supplier evaluation agent invokes the consensus pattern — running parallel evaluation against multiple supplier scoring models and synthesising a consensus recommendation.
Compliance checking agent. Evaluates the proposed purchase against the organisation’s procurement policy, trade compliance requirements, and any applicable regulatory constraints. Checks for sanctioned supplier status, export control classifications, single-source justification requirements, and budget authorisation levels. Produces a compliance clearance decision with a structured record of each check performed and its outcome.
Purchase order generation agent. Receives the approved specification match, the selected supplier, and the compliance clearance, and generates a purchase order document with all required commercial terms, delivery instructions, and approval routing metadata. For standard purchases, this agent uses a template-based generation approach. For complex contracts or non-standard terms, it flags the generated document for legal review.
The procurement orchestrator manages the sequence and parallelism of these agents. Specification matching and compliance checking can run in parallel from the outset; supplier evaluation is invoked only if no approved catalogue match is found; purchase order generation proceeds only when both a supplier selection and a compliance clearance are available. This parallel execution significantly reduces the end-to-end cycle time compared to a sequential process.
Governance Frameworks for Multi-Agent Systems
An enterprise AI governance framework 2026 must extend to cover the specific challenges that multi-agent systems introduce. Governance of a single-agent system is relatively straightforward — one reasoning thread, one audit log, one approval boundary. Multi-agent systems are more complex in each of these dimensions.
Distributed Audit Trails
Every agent in the system must maintain its own audit log: inputs received, reasoning steps taken, tool calls made, outputs produced, and confidence scores where applicable. The orchestration layer must maintain a workflow-level audit trail that correlates the agent-level logs into a coherent record of the end-to-end process. Without this correlation, auditing a multi-agent workflow is impractical — the sequence of events and decisions is distributed across multiple isolated logs.
Implement a workflow identifier that propagates through every agent in the system, enabling all agent-level events to be correlated to a single workflow execution in the audit store. This identifier should be included in every inter-agent message, every tool call, and every human escalation notification.
Human Oversight at Agent Boundaries
The governance tier model (autonomous execution, approval required, human execution) described for single-agent systems must be applied at the level of individual agent actions within a multi-agent workflow, not just at the workflow level. A procurement workflow may be largely autonomous, but the supplier evaluation agent’s selection of a non-preferred supplier above a spend threshold should trigger a Tier 2 approval gate — even if the surrounding workflow is configured for autonomous execution.
This requires that each specialist agent has configurable governance parameters, and that the orchestrator is aware of and enforces these parameters as part of its workflow management logic. The governance configuration should be externalised from the agent implementation — stored in a policy service rather than hardcoded into the agent’s system prompt — so that governance parameters can be updated without redeploying the agent.
Scope Boundaries and Tool Access Controls
In a multi-agent system, the principle of least privilege applies at the agent level. Each specialist agent should have access only to the tools required for its specific function. The document classification agent does not need write access to the ERP system. The purchase order generation agent does not need access to the supplier evaluation database. Enforcing these scope boundaries at the infrastructure level — not just through prompt instructions — limits the blast radius of an agent failure or a prompt injection attack.
Change Control and Version Management
Multi-agent systems are more complex to change safely than single-agent systems because a change to one agent can have downstream effects on agents that depend on its output. Treat multi-agent system updates with the same rigour as microservices version management: define explicit API contracts between agents, version those contracts, and test downstream agents against new versions before promotion to production.
Getting Started with Multi-Agent Architecture
For organisations moving from single-agent deployments to multi-agent systems, the transition should be evolutionary rather than a wholesale redesign.
Begin by identifying the failure modes in your existing single-agent deployments. Where does the agent struggle with tool selection? Where does context saturation degrade performance? Where does the system try to handle tasks that require genuinely different expertise in different phases? These pain points are the natural decomposition boundaries for your first multi-agent architecture.
Build your first multi-agent system using the pipeline pattern — it is the simplest to implement, test, and reason about. Introduce a supervisor orchestrator only when you have validated the individual pipeline stages in isolation. Reserve the consensus pattern for the high-stakes decisions within your workflow that justify the additional latency and cost. Move to hierarchical orchestration only when the complexity of your workflow genuinely exceeds what a single orchestration layer can manage.
Apply the same instrumentation discipline to multi-agent systems that you would to a distributed microservices architecture. Distributed tracing, structured logging, and correlation identifiers are not optional extras — they are the foundation of your ability to operate, debug, and govern the system in production.
Conclusion
The 1,445% increase in Gartner enquiries about multi-agent AI systems is not hype. It reflects a genuine architectural shift in how enterprises are approaching agentic AI: moving from single-purpose agents to orchestrated teams of specialists that can collectively execute the complex, multi-domain workflows that drive real enterprise value.
Building effective multi-agent systems requires architectural discipline — selecting the right orchestration pattern for each workflow’s characteristics, designing structured inter-agent communication protocols, implementing resilient error handling and fallback strategies, and maintaining the governance and audit infrastructure that makes autonomous agent systems trustworthy in production.
McKenna Consultants designs and implements multi-agent AI systems for enterprise clients, bringing the architectural depth and production engineering experience that the agentic AI enterprise deployment strategy requires. Whether you are evaluating multi-agent architectures for the first time or working to bring an existing deployment reliably to production scale, our AI consultancy team can help. Contact us to discuss your multi-agent requirements.
