Task Delegation Between AI Agents

The Delegation Process

Delegation in multi-agent systems follows a pattern similar to how managers assign work in human organizations. The orchestrator receives a task, analyzes its requirements, identifies which agent or agents are best suited to handle it, packages the relevant context, and dispatches the work. The key difference from human delegation is that AI delegation must be more explicit because agents cannot infer intent from body language, shared context, or organizational culture. Every piece of information the worker agent needs must be explicitly included in the delegation message.

The delegation message typically includes three components: the task description (what the agent should do), the context (all information the agent needs to do it), and the output specification (what format the agent should produce its result in). Omitting any of these components leads to predictable failures. Missing task descriptions leave the agent guessing about its objective. Missing context forces the agent to either hallucinate information or produce generic output. Missing output specifications produce results that downstream agents cannot parse or process.

Effective delegation also includes constraints that bound the agent's behavior: maximum token budget for the response, specific tools the agent is allowed to use, quality criteria the output must meet, and escalation instructions for when the agent encounters situations it cannot handle. These constraints prevent agents from consuming excessive resources, using inappropriate tools, producing substandard output, or getting stuck on tasks that require human intervention.

Capability-Based Routing

The simplest delegation strategy is capability-based routing, where each agent declares a set of capabilities and the orchestrator matches incoming tasks to agents based on capability alignment. A research agent might declare capabilities for web search, document analysis, and source evaluation. A writing agent might declare capabilities for content generation, summarization, and formatting. When a task arrives, the orchestrator identifies which capabilities it requires and routes it to the agent with the best match.

Capability matching can be implemented at different levels of sophistication. Rule-based matching uses explicit if-then logic: if the task is classified as research, send it to the research agent. This is simple and predictable but requires manually defining routing rules for every task type. LLM-based matching uses a lightweight model to analyze the task and select the most appropriate agent from a list of available agents with their capability descriptions. This handles novel task types more gracefully but adds latency and cost for the routing decision.

Hierarchical capability matching organizes agents into teams or departments, with a top-level router selecting the appropriate team and a team-level router selecting the specific agent within the team. This two-level approach scales better than flat routing because each routing decision involves fewer options, improving classification accuracy. It also mirrors organizational structures, making the system architecture more intuitive for teams that think about work delegation in hierarchical terms.

Context Packaging

Context packaging determines how much information the worker agent receives when it is delegated a task. Too little context forces the agent to work with incomplete information, producing poor results. Too much context wastes tokens and can dilute the agent's attention, causing it to focus on irrelevant details rather than the task at hand. The goal is to provide exactly the context the agent needs, nothing more and nothing less.

A practical approach is to define a context template for each agent type that specifies which fields of the shared state it needs. A research agent might need the original query, any search constraints, and the output format specification. It does not need the results of previous writing or editing agents. A writing agent might need the research results, the content outline, and style guidelines. It does not need the raw search queries or intermediate research notes. These templates ensure consistent context packaging and prevent information leakage between agents that should not have visibility into each other's intermediate work.

Context summarization is valuable when the full context is too large for the worker agent's context window. Rather than sending the complete conversation history or all previous agent outputs, the orchestrator can summarize the relevant information into a concise context package. This summarization adds a small cost for the summary generation but can significantly improve worker agent performance by presenting information in a clean, focused format rather than forcing the agent to extract relevant details from a large, noisy context.

Workload Balancing

When multiple instances of the same agent type are available, the orchestrator must balance work across them to prevent bottlenecks and minimize latency. Round-robin distribution assigns tasks to agent instances in rotating order, providing even distribution regardless of task complexity. This is simple but can lead to uneven performance when tasks vary significantly in processing time because some instances accumulate backlogs of complex tasks while others sit idle after quickly processing simple tasks.

Queue-depth-based distribution routes new tasks to the instance with the shortest pending queue, adapting to varying task complexity by naturally directing work away from overloaded instances. This produces more even latency distribution but requires real-time visibility into each instance's queue state, which adds infrastructure complexity.

Capability-weighted distribution considers not just queue depth but also how well each instance's recent performance matches the incoming task's requirements. If one instance of a research agent has been performing better on financial topics while another performs better on technical topics, the orchestrator can route financial research to the first instance and technical research to the second. This specialization within a specialist produces modest but measurable quality improvements in systems that handle diverse task types within a single agent category.

Handling Delegation Failures

Delegation can fail at several points: the routing decision might send the task to the wrong agent, the context package might be incomplete or malformed, the worker agent might fail during execution, or the worker agent might produce output that does not meet quality criteria. Each failure type requires a different recovery strategy.

Routing failures are detected when the worker agent's output is irrelevant to the original task or when the worker agent explicitly signals that the task is outside its capabilities. Recovery involves re-routing the task to an alternative agent, typically with a note about why the first routing attempt failed so the next agent has additional context. Good routing agents include a confidence score with their routing decisions, allowing the orchestrator to proactively try multiple agents for low-confidence classifications.

Execution failures include API errors, timeouts, and model failures. These are typically handled with automatic retry logic, using exponential backoff to avoid overwhelming rate-limited APIs. After a configurable number of retries, the orchestrator falls back to an alternative agent or escalates to human review.

Quality failures occur when the worker agent completes successfully but produces output that fails validation checks. The orchestrator can retry the same agent with additional guidance (including feedback about what was wrong with the first attempt), route to a more capable agent using a higher-tier model, or break the task into smaller subtasks that are each within the agent's reliable capability range. Quality validation should be automated wherever possible, using format checks, consistency checks, and evaluator agents that assess output quality against defined criteria.