Delegation is an interaction pattern in which one actor instructs another to perform a specific action. It is the imperative form of communication in agentic systems—a command that carries intent, parameters, and context, and expects the receiving actor to do something about it. When a user tells an agent to “summarize this document,” that’s delegation. When an orchestrator assigns a research task to a specialist agent, that’s delegation. When a workflow step hands off work to the next agent in a sequence, that’s delegation too.
Delegation is arguably the most fundamental interaction pattern in agentic systems, because it’s how work gets initiated. Without delegation, agents sit idle. Every task an agent performs—from answering a question to orchestrating a complex multi-step process—begins with some form of delegation: an instruction from a user, another agent, or an automated trigger that says “here’s what I need you to do.”
How Delegation Works
A delegation message has three essential components: intent, parameters, and context. The intent describes what needs to happen—“create a ticket,” “analyze this dataset,” “draft a response.” The parameters provide the specifics—which dataset, what kind of analysis, what constraints apply. The context supplies the background information the receiving agent needs to execute effectively—prior conversation history, relevant policies, the reason behind the request.
The sender of a delegation message expects a result. This distinguishes delegation from notification, where the sender broadcasts information without expecting any specific action in return. When you delegate work to an agent, you expect it to either complete the task and report back, or explain why it couldn’t. This expectation of outcome creates an implicit contract between sender and receiver—one that becomes explicit in well-designed agentic systems through defined input schemas, output formats, and error handling conventions.
In practice, delegation can be synchronous or asynchronous. A user typing a message in a chat interface and waiting for a response is synchronous delegation—the user blocks until the agent replies. An orchestrator dispatching a research task to a specialist agent and continuing with other work while it waits is asynchronous delegation—the orchestrator doesn’t block, and the result arrives later. The choice between synchronous and asynchronous delegation has significant implications for system architecture, error handling, and user experience.
Delegation in Single-Agent and Multi-Agent Systems
In a single-agent system, delegation flows in one direction: from user to agent. The user provides instructions, the agent executes. This is the simplest form of delegation and the one most people are familiar with from interacting with chatbots and AI assistants.
In multi-agent systems, delegation becomes far more interesting—and more complex. An orchestrator agent might receive a high-level objective from a user, decompose it into subtasks, and delegate each subtask to a different specialist agent. A research task goes to the research agent. A data analysis task goes to the analytics agent. A writing task goes to the content agent. Each of these agents might further delegate sub-subtasks to other agents or tools, creating a delegation chain that can be several levels deep.
This cascading delegation is where the pattern’s power and its risks both escalate. Power, because complex work can be decomposed and distributed efficiently across specialized agents. Risk, because each delegation hop introduces the possibility of miscommunication, context loss, or unintended scope expansion. An orchestrator that delegates a vaguely defined task to a specialist agent may get back something quite different from what was intended—and if that specialist further delegated to another agent, the drift compounds.
Delegation vs. Other Interaction Patterns
Understanding delegation requires understanding what it is not. The four interaction primitives—delegation, retrieval, notification, and conversation—each serve a different communication purpose, and choosing the right one matters.
Delegation says “do this.” It is imperative and expects action. The sender wants something to change in the world as a result of the message.
Retrieval says “tell me this.” It is interrogative and expects information. The sender wants to know something but doesn’t want anything to change.
Notification says “this happened.” It is declarative and expects nothing. The sender is announcing a state change without directing any specific response.
Conversation says “let’s work through this together.” It is collaborative and expects iterative exchange. The sender and receiver engage in a sustained, contextual dialogue.
The distinctions matter for system design because each pattern has different requirements for error handling, timeout management, and resource allocation. A delegation that fails needs retry logic or escalation. A retrieval that fails can often be retried transparently. A notification that fails may simply be logged. A conversation that fails mid-stream needs state recovery.
The Quality of Delegation
Not all delegation is created equal, and the quality of a delegation message directly impacts the quality of the result. Vague delegation produces vague results. “Handle this customer issue” gives the receiving agent almost nothing to work with. “Investigate why customer #4521’s last three invoices show duplicate charges, check against the billing system records, and draft a resolution recommendation” gives it everything it needs.
This mirrors human organizations perfectly. A manager who delegates effectively provides clear objectives, relevant context, success criteria, and appropriate constraints. A manager who delegates poorly throws work over the wall and hopes for the best. The same principle applies to agent-to-agent delegation, with the added challenge that agents can’t read between the lines the way experienced human colleagues can.
In enterprise contexts, this means that the quality of delegation messages—whether from users to agents or from orchestrators to specialists—is one of the most consequential factors in overall system performance. Organizations that invest in well-defined task schemas, clear instruction templates, and rich context passing get dramatically better results from their agentic systems than those that treat delegation as an afterthought.
Delegation and Autonomy
Delegation has an inherent relationship with autonomy. The degree of specificity in a delegation message reflects how much autonomy the sender grants to the receiver. “Process this refund for $47.50 to the customer’s original payment method” is low-autonomy delegation—the agent has little room for interpretation. “Resolve this customer’s billing complaint in whatever way you think is best” is high-autonomy delegation—the agent has wide discretion.
The right level of delegation specificity depends on the trust relationship between sender and receiver, the criticality of the task, and the maturity of the receiving agent. Early in an agent’s deployment, tighter delegation with more explicit instructions is appropriate. As the agent proves reliable and the organization builds confidence, delegation can become more abstract, granting the agent greater latitude to determine its own approach.
This progression from specific to abstract delegation is one of the practical manifestations of increasing autonomy in enterprise agentic systems. It doesn’t happen automatically—it requires deliberate governance decisions about which agents are trusted with which levels of discretionary authority.
Why Delegation Matters in Enterprise Contexts
In enterprise environments, delegation is the mechanism through which organizational intent flows into agent execution. When a business process requires an agent to take action—whether triggered by a user request, a scheduled event, or another agent’s output—it arrives as a delegation message. This makes delegation design a critical concern for anyone building production agentic systems.
Well-designed delegation patterns create clear accountability. When every task has a defined sender, a defined receiver, explicit parameters, and an expected outcome, it’s straightforward to trace what happened, why it happened, and who (or what) initiated it. This traceability is essential for compliance, debugging, and continuous improvement.
Poorly designed delegation patterns create the opposite: ambiguous responsibility, untraceable decisions, and agents that take actions nobody can explain or attribute. In regulated industries, this isn’t just an operational inconvenience—it’s a compliance risk.
Also Known As
Delegation appears under various names across different platforms and communities. You’ll encounter it as commands (particularly in CQRS and event-driven architectures), tasks or task assignments (in workflow and orchestration contexts), requests (in request-response communication patterns), instructions (when emphasizing the directive nature), or invocations (when emphasizing the execution trigger). In some multi-agent frameworks, delegation is called dispatching or routing. Regardless of terminology, the core concept remains the same: one actor directing another to perform specific work.
Key Takeaways
Delegation is the imperative interaction pattern that initiates work in agentic systems. It carries intent, parameters, and context from a sender to a receiver, with an expectation of outcome. In multi-agent systems, delegation cascades through chains of specialized agents, creating powerful decomposition but also introducing risks of context loss and scope drift. The quality of delegation—how clearly objectives, constraints, and context are communicated—is one of the most significant determinants of agent effectiveness. In the agentic primitives framework, delegation sits alongside retrieval, notification, and conversation as the four interaction patterns that define how actors communicate, and it is the pattern most directly tied to getting things done.