Single-agent systems are simple to reason about. One agent, one task, one output. But most real-world problems are not single-agent problems. They involve multiple concerns, multiple data sources, multiple skill domains, and multiple stages of processing. Trying to solve these with a single monolithic agent is like trying to build a distributed system with a single function: it works for small problems and collapses for big ones.
Multi-agent orchestration is the discipline of coordinating multiple specialized agents to solve problems that no single agent can handle effectively alone. It is the architectural layer that turns individual agent capabilities into system-level solutions.
The field is still young, but clear patterns have emerged. After building multi-agent production systems across dozens of client engagements, we have identified three core orchestration patterns that cover the vast majority of use cases: Delegation, Routing, and Swarm. Each pattern solves a different problem, has different architectural characteristics, and is appropriate for different scenarios.
Pattern 1: Delegation
The Problem It Solves
Complex tasks that can be decomposed into subtasks requiring different specializations. A single agent cannot be an expert at everything. A delegation pattern assigns subtasks to specialist agents who are optimized for narrow, well-defined functions.
Architecture
The Delegation pattern uses a hierarchical structure:
Orchestrator Agent
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+-- Specialist Agent A (e.g., Data Extraction)
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+-- Specialist Agent B (e.g., Business Rule Validation)
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+-- Specialist Agent C (e.g., Document Generation)
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+-- Specialist Agent D (e.g., Integration/API)
The Orchestrator Agent receives the top-level task, decomposes it into subtasks, assigns each subtask to the appropriate specialist, collects results, handles inter-task dependencies, and assembles the final output.
Each Specialist Agent has:
- A narrow, well-defined mandate (what it does)
- Specific tools and data access (what it has access to)
- Defined input/output contracts (how it interfaces)
- Quality thresholds (what “good enough” means for its output)
How It Works in Practice
Consider a production system we built for processing insurance claims. The top-level task is: “Process this claim submission and produce an adjudication recommendation.”
The Orchestrator decomposes this into:
Document Agent: Extract structured data from the claim documents (medical records, receipts, policy documents). This agent specializes in document understanding with domain-specific extraction rules for insurance document types.
Policy Agent: Retrieve the applicable policy and determine coverage rules. This agent has access to the policy database and understands the coverage determination logic for different policy types.
Validation Agent: Cross-reference extracted claim data against policy coverage rules and flag discrepancies. This agent specializes in applying business rules and producing structured validation reports.
Fraud Detection Agent: Analyze the claim for patterns consistent with known fraud indicators. This agent has access to historical fraud data and applies anomaly detection models.
Recommendation Agent: Synthesize the outputs of all other agents into an adjudication recommendation with supporting evidence and confidence score. This agent specializes in decision synthesis and explanation generation.
The Orchestrator manages the execution sequence (some tasks are parallel, some are sequential), handles failures (if the Document Agent cannot extract data from a page, it escalates to a human-in-the-loop review), and assembles the final recommendation package.
Design Considerations
Orchestrator complexity. The Orchestrator is the most critical component. If it decomposes tasks poorly, specialist outputs will not compose correctly. Orchestrator design requires deep understanding of the task domain and the capabilities of each specialist.
Inter-agent contracts. Each specialist must produce output in a format that downstream agents and the Orchestrator can consume. Define these contracts explicitly, using schemas, not just informal descriptions. When Agent A’s output is Agent B’s input, any format mismatch becomes a system failure.
Failure propagation. When a specialist fails, the Orchestrator must decide: retry, use a fallback, proceed without that input, or fail the entire task. These decisions should be predefined in the orchestration logic, not left to the Orchestrator agent to figure out at runtime. Predefined failure handling is more reliable than dynamic failure reasoning.
Specialist scope. Specialists should be narrow enough to be excellent at their task but not so narrow that the number of specialists becomes unmanageable. A good heuristic: if two specialists always run together and one’s output is always the other’s input, they should probably be one specialist with a richer internal pipeline.
When to Use Delegation
- Tasks that decompose cleanly into independent or sequential subtasks
- Problems where different subtasks require fundamentally different capabilities
- Systems where specialist quality matters more than cross-cutting reasoning
- Production workflows with well-defined processing stages
Pattern 2: Routing
The Problem It Solves
Incoming requests that vary in type, complexity, or domain, and need to be directed to the right handler. Not every request needs the same agent. Routing matches tasks to agents based on the characteristics of the task.
Architecture
The Routing pattern uses a classifier-dispatcher structure:
Incoming Request
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v
Router (Classifier)
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+-- Handler Agent A (Simple queries)
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+-- Handler Agent B (Complex analysis)
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+-- Handler Agent C (Domain-specific tasks)
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+-- Handler Agent D (Escalation/human handoff)
The Router analyzes the incoming request and directs it to the Handler Agent best suited to process it. Unlike Delegation, the request is handled by a single handler, not decomposed across multiple specialists.
How It Works in Practice
Our Veracall voice AI platform uses the Routing pattern extensively. When a voice interaction comes in, the system needs to determine what kind of request it is and route it to the appropriate handler:
Intent Classification: The Router analyzes the caller’s initial statement and classifies the intent: account inquiry, technical support, billing question, appointment scheduling, general information, or escalation to human.
Handler Selection: Based on the classification, the Router selects the Handler Agent with the right domain knowledge, tool access, and conversational style:
- Account Inquiry Handler has access to CRM data and can pull up account details
- Technical Support Handler has access to knowledge bases and troubleshooting workflows
- Billing Handler has access to payment systems and can process adjustments
- Scheduling Handler has access to the appointment system and can check availability
- Human Escalation routes to a live agent with full context transfer
Confidence-Based Routing: The Router does not just pick the most likely handler. It evaluates its classification confidence. High-confidence classifications (above 0.85) route directly. Medium-confidence classifications (0.60-0.85) route to the handler but with a lower autonomy level (more frequent confirmation checks). Low-confidence classifications (below 0.60) route to a triage handler that asks clarifying questions before re-routing.
Dynamic Re-routing: If a handler determines mid-conversation that the request is outside its domain, it can request re-routing through the Router. This handles the common case where a caller starts with one question but their real need is something different.
Design Considerations
Classification accuracy. The Router’s classification accuracy is the most important performance metric. A misrouted request wastes the handler’s capacity and degrades the user experience. Invest heavily in classification quality: training data, evaluation sets, and continuous monitoring of routing accuracy in production.
Handler overlap. In practice, categories are not perfectly distinct. A billing question might involve technical details. An account inquiry might involve scheduling. Define clear boundaries for each handler’s scope and establish hand-off protocols for edge cases that span categories.
Load balancing. When multiple handlers of the same type exist (for scalability), the Router also performs load balancing. This adds complexity but is necessary for production-scale systems.
Fallback paths. Every routing decision needs a fallback. If no handler matches with sufficient confidence, the request should route to a generalist handler or a human, not fail silently. The fallback path is the safety net that prevents the system from dropping requests.
Model selection as routing. Routing is not just about agent selection. In many systems, routing also determines which underlying model to use. Simple requests might route to a smaller, faster, cheaper model. Complex requests route to a more capable, more expensive model. This cost-performance optimization is a practical application of the routing pattern that we use across our platforms.
When to Use Routing
- Systems that handle varied request types
- Customer-facing interfaces where request classification drives the experience
- Cost optimization scenarios where different request types warrant different compute resources
- Multi-domain systems where domain-specific knowledge matters more than cross-domain synthesis
Pattern 3: Swarm
The Problem It Solves
Problems where the best approach is not known in advance and benefits from multiple perspectives, parallel exploration, or collaborative refinement. Swarm is the pattern for problems that are too complex or too ambiguous for a single agent or a predefined decomposition.
Architecture
The Swarm pattern uses a collaborative structure:
Problem Statement
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Swarm Controller
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+-- Agent Instance 1 (Approach A)
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| +-- evaluates --> Shared Evaluation Space
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+-- Agent Instance 2 (Approach B)
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| +-- evaluates --> Shared Evaluation Space
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+-- Agent Instance 3 (Approach C)
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| +-- evaluates --> Shared Evaluation Space
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v
Synthesis Agent
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v
Final Output
Multiple agent instances work on the same problem simultaneously, potentially with different approaches, different models, or different context configurations. Their outputs are evaluated against defined criteria, and the best solution (or a synthesis of multiple solutions) is selected.
How It Works in Practice
We use the Swarm pattern in our internal development process for problems where the solution approach is uncertain. A recent example: designing the data pipeline for a client’s real-time analytics system.
The problem specification defined the requirements (data sources, processing rules, latency targets, throughput requirements) but left the architectural approach open because multiple valid approaches existed.
Swarm Initialization: The Swarm Controller launched three agent instances, each configured with different architectural starting points:
- Agent 1: Stream processing approach (Kafka + Flink)
- Agent 2: Event-driven microservices approach (event bus + specialized processors)
- Agent 3: Hybrid batch/stream approach (Lambda architecture variant)
Parallel Exploration: Each agent developed its approach independently, producing architecture documents, key component specifications, and estimated resource requirements.
Evaluation: The Shared Evaluation Space scored each approach against predefined criteria: latency compliance, throughput capacity, operational complexity, cost estimate, and scalability characteristics. This evaluation was partially automated (latency and throughput could be estimated from the architecture) and partially human (operational complexity and maintainability required human judgment).
Synthesis: The Synthesis Agent, in this case working closely with a human architect, combined elements from the top two approaches. The stream processing core from Agent 1 was combined with the specialized processor pattern from Agent 2, producing an architecture that neither agent had proposed independently.
Specification Generation: The synthesized architecture was formalized into specifications that could then be implemented using the Delegation pattern, specialist agents building each component.
Design Considerations
Diversity of approaches. The value of Swarm comes from exploring genuinely different approaches. If all agents produce the same solution, the pattern adds cost without value. Ensure diversity through different model configurations, different context (emphasize different constraints or priorities), or different starting points.
Evaluation rigor. The evaluation criteria must be defined before the swarm launches, not after seeing results. Post-hoc evaluation criteria create bias toward whichever solution the evaluator already prefers. Pre-defined criteria ensure objective comparison.
Cost management. Swarm is the most expensive pattern because it runs multiple agents in parallel on the same problem. Use it for high-value decisions where the cost of a suboptimal approach exceeds the cost of multi-agent exploration. Do not use it for routine implementation tasks where the approach is clear.
Convergence criteria. Define when the swarm stops. Options include: fixed number of iterations, minimum quality threshold reached, convergence of approaches (all agents arriving at similar solutions), or time/budget limit. Without convergence criteria, swarms can run indefinitely without improving.
Human synthesis. In current practice, the synthesis step almost always involves human judgment. Combining the best elements of multiple agent-generated approaches requires the kind of holistic reasoning and domain-aware judgment that agents do not yet do reliably. Design the pattern with human involvement at the synthesis stage.
When to Use Swarm
- Architectural design decisions where multiple valid approaches exist
- Optimization problems where the search space is large
- Creative problems where diversity of solutions has value
- High-stakes decisions where the cost of a wrong approach justifies the cost of parallel exploration
Composing Patterns
In production systems, these patterns rarely appear in isolation. They compose:
Routing into Delegation. A customer request is routed to the appropriate domain handler (Routing), which then decomposes the task into subtasks handled by specialists (Delegation). This is the most common composition in customer-facing systems.
Swarm into Delegation. Multiple approaches are explored for a system design (Swarm), the best approach is selected and specified, and the implementation is decomposed into specialist tasks (Delegation). This is common in greenfield development.
Delegation with internal Routing. A Delegation orchestrator assigns a subtask to a specialist pool, and a Router within that pool selects the specific specialist instance based on task characteristics. This is common in systems that need to scale specialist capacity.
Routing with Swarm fallback. Routine requests are routed directly to handlers (Routing), but novel or ambiguous requests that no handler matches with high confidence are sent to a Swarm for multi-agent exploration. This handles the long tail of unusual requests.
Architectural Principles
Across all three patterns, several architectural principles apply:
Define contracts, not implementations. The interface between agents should be defined by input/output contracts, not by implementation details. This allows agents to be swapped, upgraded, or scaled independently.
Make orchestration observable. Log every routing decision, every delegation assignment, every swarm evaluation. Observability is how you debug multi-agent systems and how you improve them over time. At CONFLICT, we instrument every orchestration decision with structured logging that feeds into our monitoring infrastructure.
Design for partial failure. In a multi-agent system, individual agents will fail. The orchestration layer must handle this gracefully: retry, fallback, degrade, or escalate. Never design an orchestration that assumes all agents always succeed.
Separate orchestration from intelligence. The orchestration logic (who does what when) should be separate from the intelligence logic (how each agent does its work). This separation allows you to change the orchestration without changing the agents and vice versa.
Start simple. Most problems do not need Swarm. Many do not need Delegation. Some do not even need Routing. Start with the simplest pattern that solves the problem and add complexity only when measurement shows it is needed. Over-architecting multi-agent systems is as wasteful as over-architecting any other software system.
These patterns are not theoretical. They are operational designs that we use daily in building production AI systems. The key is matching the pattern to the problem and implementing it with the same engineering rigor you would apply to any distributed system. Agent orchestration is distributed systems engineering with probabilistic components. That makes it harder, not easier, and it makes architectural discipline more important, not less.
Choose the right pattern. Define the contracts. Build the observability. Design for failure. Measure everything. That is how multi-agent systems move from impressive demos to reliable production infrastructure.
