coordinationmediumcommon

Dynamic Task Routing Pattern

Intelligent task distribution based on real-time agent capabilities

Overview

The Challenge

Static task allocation wastes resources and creates bottlenecks. Systems need intelligent routing based on real-time agent capabilities, workload, and task characteristics.

The Solution

Implement a routing layer that analyzes incoming tasks and dynamically routes them to the most appropriate agent based on capability matching, current load, historical performance, and cost.

When to Use
  • Heterogeneous agent pools with different specializations
  • Variable workload patterns
  • When optimizing for latency or cost
  • Systems requiring high availability
When NOT to Use
  • Homogeneous agent pools
  • When all agents must see all tasks
  • Strictly ordered workflows

Trade-offs

Advantages
  • +Optimal resource utilization
  • +Automatic load balancing
  • +Graceful degradation on failures
  • +Can optimize for multiple objectives
Considerations
  • −Routing logic adds latency
  • −Requires capability metadata
  • −Can make debugging harder
  • −Cold start for new agents
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Deep Dive

Overview

Dynamic routing ensures tasks reach the most appropriate agent in real-time, optimizing for capability match, load, and performance.

Routing Strategies

Semantic Routing

  • Embed task descriptions and agent capabilities
  • Route based on semantic similarity
  • Handles novel task types gracefully

Rule-Based Routing

  • Explicit rules map task types to agents
  • Fast and predictable
  • Requires maintenance as system evolves

ML-Based Routing

  • Learn optimal routing from historical data
  • Can capture complex patterns
  • Requires training data and monitoring

Implementation Components

Gatekeeper Agent

Entry point that analyzes and routes incoming requests.

Capability Registry

Centralized or distributed store of agent capabilities.

Load Monitor

Tracks agent utilization and availability.

Fallback Chain

Defines backup agents when primary is unavailable.

References

Code Examples

Semantic routingpython
async def route_task(task, agents):
    """Route task to best-fit agent."""
    # Get embeddings for task and agent capabilities
    task_embedding = embed(task.description)

    scores = []
    for agent in agents:
        # Capability match
        cap_score = cosine_sim(task_embedding, agent.capability_embedding)
        # Load factor (prefer less loaded)
        load_score = 1 - agent.current_load
        # Historical performance on similar tasks
        perf_score = agent.get_performance(task.type)

        total = 0.5 * cap_score + 0.3 * load_score + 0.2 * perf_score
        scores.append((agent, total))

    return max(scores, key=lambda x: x[1])[0]
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Considerations

Monitor routing decisions for bias. Ensure new agents can be discovered and receive traffic.

PreviousModel Context Protocol (MCP) Pattern
NextSub-Agent Delegation Pattern
Dimension Scores
Safety
3/5
Accuracy
4/5
Cost
4/5
Speed
3/5
Implementation
Complexitymedium
Implementation Checklist
Agent capability registry
Load monitoring
Routing algorithm
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Tags
routingload-balancingcapabilitydynamicorchestration

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