Lead AI agent delegates research to a specialist agent
A coordinator AI employee receives a complex request, breaks it into subtasks, and assigns each to a specialized agent that handles it end to end.
Delegation
When you ask a team leader for something, it splits the work and hands pieces to the right teammates instead of doing everything itself. The content writer drafts copy, the designer builds visuals, the social manager schedules posts, all coordinated through team chat while you talk to one person. Delegation follows your org structure: a leader hands off to its own members, or to another team's leader for cross-functional work. Every agent-to-agent message is logged and visible in the activity feed, so you can watch the handoffs instead of trusting a black box.
AI employees do not work in isolation. They communicate with each other through team chat, the same way a real team collaborates. When a leader receives a complex request, it breaks the work into pieces and delegates to the right specialists. The content writer drafts the copy, the designer creates the visuals, and the social media manager schedules the posts. All coordinated through team chat, without you managing each step.
Delegation follows your org structure. Team leaders assign work to their own members and hand off cross-functional tasks to other team leaders. If you ask the marketing lead to launch a campaign, it might delegate writing to its content specialist and coordinate with the sales team lead on messaging alignment. Each delegation creates a visible chain you can follow in the activity feed.
Every agent-to-agent conversation is visible. You can watch your employees discuss, divide work, ask each other questions, and share results. It is full transparency into how your AI team collaborates, so you spot coordination issues before they become output problems.
You still talk to one person. Delegated exchanges are folded into the leader's own chat timeline by default, each message attributed to the teammate who actually wrote it, so the conversation reads like a team working in front of you rather than a sealed box. A toggle in the chat header hides them again whenever you only want the leader's answers.
There are dedicated views for the fuller picture. You can open everything two employees have said to each other, or every internal exchange across a whole team, which is what you want when a deliverable came out wrong and you need to find the handoff where the brief drifted rather than re-running the work and hoping for better.
The practical result is that one instruction moves several people at once. A brief that would take a single employee a long sequential grind gets split, worked in parallel, and reassembled by the leader into one deliverable. You gave one instruction and got one answer, and the coordination in between happened with no meeting, no status ping, and no handoff you had to chase.
AI Employees That Delegate to Each Other
Sistava supports multi-agent collaboration where AI employees can send tasks to each other, coordinate on shared work, and report results back. One agent can act as a coordinator, breaking down a project and delegating subtasks to specialist agents on the team.
This is how you scale beyond a single agent. A chief of staff AI employee can manage a team of specialist agents, each focused on a different domain, and synthesize their outputs into a single coherent deliverable.
Parallel Execution Across the Workforce
When one AI employee delegates to several others simultaneously, work happens in parallel rather than sequentially. A task that would take a single agent an hour can be completed in minutes when split across a coordinated team of autonomous agents.
Each agent works independently on its assigned piece, using its own tools and integrations, and reports back when done. The coordinating agent assembles the results, resolves any conflicts, and delivers the final output.
Transparent Communication Logs
Every message between AI employees is logged and visible to you. You can see exactly what was delegated, what each agent said, and how the work was assembled. There is no hidden agent-to-agent traffic you cannot inspect.
This auditability is critical in production environments. You can review any multi-agent conversation, understand what decisions were made at each step, and identify where a workflow went wrong if something did not go as planned.
How It Works
AI leader agents break down complex requests and delegate to the right specialists automatically, with every handoff visible in the activity feed.
When you assign a goal to a team leader, the AI agent reasons about which subtasks it involves and which team member is best positioned for each one. It delegates with a clear brief and tracks the result. Specialists receive the delegated task, complete it, and return the output to the leader. The leader assembles the pieces and delivers the final result to you.
Every delegation creates a visible chain in the activity feed. You can follow the entire path from your original request to each delegated subtask to the final output. When an agent needs information from another one, it asks directly through the same system. This is not behind-the-scenes magic, it is structured coordination with a full audit trail you can inspect at any time.
A leader is a normal employee you talk to normally, so none of this changes how you work. You ask for the outcome, and the split, the briefs, and the reassembly happen underneath. Delegated replies appear in the leader's timeline attributed to the teammate who wrote them, and a header toggle collapses them back to the leader's answers alone when you want the short version.
Use Cases
Sales agent hands off qualified leads to CRM agent
After a discovery conversation, the sales AI agent passes the lead summary to a CRM agent that updates records, sets follow-ups, and notifies the rep.
Marketing team runs multi-agent content pipeline
A strategy agent briefs a writing agent, which hands output to an editing agent, which sends finished content to the publishing agent, all without human steps in between.
Ops team coordinates agents across departments
An orchestrator AI agent routes incoming requests to the right departmental agent, finance, HR, or legal, and collects their responses into one reply.
Comparison
| Before | After |
|---|---|
| Each AI agent works in isolation, no handoffs. | Agents delegate to each other, covering complex multi-step workflows. |
| Human has to relay output from one agent to the next. | Agent-to-agent delegation removes the human from every handoff. |
| Specialist agents sit idle while generalist agents are overloaded. | Delegation routes work to the right agent automatically. |
| Multi-step workflows require a human coordinator. | The lead AI employee orchestrates the full pipeline end to end. |
FAQ
How does agent delegation actually work?
A coordinating AI employee sends a structured task message to one or more other agents on the team. Each receiving agent executes the task using its own skills and tools, then reports results back to the coordinator, which synthesizes the outputs.
Can I see what AI employees said to each other?
Yes. All inter-agent communication is logged and available in the conversation history. You can read every message exchanged between agents, including task assignments, clarifying questions, and result reports.
Do all AI employees need the same skills for team chat to work?
No, and that is the point. Team chat works best when each agent has different skills. A research agent, a writing agent, and a data analysis agent can collaborate on a single project without needing to duplicate each other's capabilities.
Is there a limit to how many agents can work together on a task?
Team size depends on your plan. Each agent in a collaborative task runs independently and uses its own compute allocation. Larger teams of agents can be configured for enterprise workflows.
Can one AI agent assign work to another AI agent?
Yes, leader AI employees can delegate tasks to specialist agents on the same team, just like a human manager. The leader breaks down work and hands off subtasks to the right agents automatically.
Do I have to talk to each AI employee separately, or can I just ask the team leader?
Ask the leader. It works out which teammates the request touches, briefs each of them, and comes back with the assembled result, so you hold one conversation instead of five. The individual exchanges stay readable in the same thread if you want to see who did which part.
Can a team leader hand work to another team, not just its own members?
Yes. A leader delegates inside its own team and hands cross functional work across to another team's leader, following the org chart you built. That is how a request touching both marketing and sales gets covered without you routing it between them by hand.
Do I need a whole team before delegation is useful?
Delegation needs a leader with at least one teammate, so a single employee working alone simply does the work itself. You can start with one specialist and add teammates later, and the leader begins handing work out as soon as there is somebody to hand it to.
I gave the marketing lead a campaign brief and it distributed the work to my writer, designer, and analyst on its own. I approved the final output. The whole thing ran without a meeting.
Where Delegation fits
Delegation is part of How they coordinate as a team.
Run your AI workforce the way you run a good team. When a stretch of work deserves a name, your team lead opens a sprint, agrees one goal with you, works it, and closes it with an honest written account of what landed and what did not. Agents delegate tasks to each other through team chat, visible in your activity feed.
- How they coordinate as a team: Sprints, one goal, and delegation. Built in.
Read the guide
More in Sprints Planning
- Give Your Team an AI Leader: Every team you hire gets one employee assigned as its leader, marked with a crown badge. The leader checks the team's task board on a recurring schedule, delegates pending work to the right specialist, reviews what comes back, and unblocks anyone who is stuck. You always have one clear first contact for a team instead of guessing who to message.
- Sprints: Give a team's work a goal and a time window instead of an endless task list. Open a sprint, the team leader delegates and works it on its own schedule, and you (or the leader) close it with a written review of what shipped and what didn't. Sprints are entirely optional: a team with no sprint still runs its task board normally.
- Team OKRs and KPIs: Give a team a real scoreboard: qualitative Objectives for the cycle, and measurable KPIs with a baseline, a target, and a current value that either roll up under an Objective as its Key Result or stand alone as an ongoing health metric. Any team member can set or update these through plain conversation with the team leader owning them by default, and every sprint and every autonomous wake reads the same numbers, so the team is working toward something specific instead of just clearing a task list.
- Team Goals & Guidelines: Give a team a shared Vision statement and a set of Guidelines that every member reads before they act. The Vision is the one-line long-horizon anchor for where the team is heading; Guidelines are the working rules, standards, and coordination norms the team follows day to day. Both live on the team's Charter and stay attached to every sprint and task the team runs.
- Start a Task Now: Every open task has a Start now button. Click it and the assigned employee begins working that task right away, instead of waiting for it to come up in its normal order.
- Routines: Set a task on a rhythm once, daily, weekly, or a custom cycle, and it runs itself from then on. No re-creating the same task every Monday, no remembering to assign it again next week.
- Playbooks: Every employee leads with a short set of proven playbooks, the highest-value jobs they can run start to finish for the role you hired them for.
- Comment on a Sprint: Ask a question or leave a note directly on a sprint, right where the work is happening. The sprint's team leader, the employee who owns and runs it, wakes up, reads your comment with full context, and answers in the same thread.
- The Weekly Sprint Check-In: Once a week your team lead looks at the state of the work and raises the sprint question with you directly: is there a stretch of work worth naming and turning into a sprint, or has the open sprint run past its date and earned a close? You answer in a sentence and the team acts on it. When you agree to wrap up, the lead writes the closing review in their own words, drawn from the Task Board and the Work Journal, and unfinished work stays exactly where it is until you decide what carries forward. Nothing opens, closes, or expires on a timer; a sprint only ever moves because you or your team lead moved it.
- Work Toward Your OKRs: Give your AI team objectives and key results in plain language, the same way you'd brief a human manager, and the team leader plans every sprint against them. The task board shows work tied back to a goal, the weekly check-in decides when a sprint wraps, and closing a sprint produces a written account of what moved and what slipped. Ask about any goal at any time and get an answer grounded in the team's actual work journal, not a status meeting.