How Do Agents Delegate Tasks to Each Other?

Anything a single agent can do eventually becomes an organizational coordination problem at scale. An engineer agent writes a software component, another agent tests it, a third documents it, and a fourth deploys it. This flow is not random. It follows a hierarchy of task handoffs, approvals, and verifications among agents.

This is where A2A — Agent-to-Agent — comes into play. Just as a manager in a human organization distributes tasks, reviews outcomes, and holds people accountable, agents do the same with each other. But the terms have to be explicit, traceable, and auditable. Without that structure, A2A simply becomes a more elaborate form of shadow AI.

Delegation Is a Defined Process, Not Just Authority

In the shadow AI world, everyone works alone. As we saw in How Many AI Agents Are Running in Your Company, and Does Anyone Know?, employees bring their own tools and carry their own weight. In an agent environment, delegation is not about comfort. It is a governance transformation.

When agent A hands off work to agent B, three things must be explicit:

1. Task definition: what is being produced and what is not.
2. Approval chain: who verifies the result and who approves it.
3. Feedback loop: under what conditions the output is accepted or rejected.

These three points are no different from a human work order. In What Is an Agent: From AI Tool to AI Agent, we distinguished tools from agents. A tool only takes input. An agent carries a defined responsibility, model, capability, and policy. Delegation depends on that definition.

Approval Flow and Control

Agent A generates code, agent B runs tests, agent C produces documentation, and agent D deploys. Each step can have an approval gate. If tests fail, the workflow does not proceed to deployment. This rule set is defined in the policy.md we discussed in Policy and Agent State.

Policy determines what agents can delegate to each other, when they must stop, and who approves the final output. Without this structure, A2A delegation does not improve governance. It just automates invisible work.

The accountability line also stays intact. In The Accountability Line, we explained that every output must have an owner. When agents delegate to each other, the line does not break. The last agent to touch the output is not automatically responsible. The agent that initiated the flow, or the human that directed it, remains accountable.

Parallel with Human Delegation

In a software team, a tech lead assigns tasks, a junior opens a pull request, a peer reviews it, and a lead merges it. Agents follow the same steps. The difference is that every step is recorded systematically and governed automatically by policy.

Human delegation usually follows an upward hierarchy. Between agents, the same hierarchy can be built. Higher-authority agents delegate to lower-authority agents, review their outputs, and intervene when needed. This structure is a natural extension of the org chart we proposed in Why the Org Chart Must Include Agents?.

The Point Where the First Four Days Converge

Day one named the problem. Day two separated tools from agents. Day three placed agents inside the org chart. Day four set the rules with policy and state management. Day five is where all of them operate together: agents delegate to each other, but they do so in a defined, traceable, and accountable way.

As we noted in Agents Must Operate Under Human Accountability, the real strength of hybrid teams is a clear accountability line. A2A delegation is the most detailed operational model for that line. When an agent delegates to another, it shares responsibility — but only within a monitored layer.

Once this mechanism is in place, the organization is no longer running invisible AI through personal browser tabs. Every step is recorded, every task is defined, and every output can be audited.