Run Your Team With AI Agents: A Mission Control

What Is an Agent Mission Control?

A mission control is a central system where specialized agents handle different categories of recurring team work. Instead of you personally drafting every update, researching every question, reviewing every draft, and routing every request, you have a set of purpose-built agents that each do one thing well and hand off to each other when tasks cross boundaries.

The concept sounds complex, but the practical version is surprisingly simple to start: a small set of prompts, each with a clear role and a defined input format, used consistently by you and your team. The "mission control" is just a shared document where those agents live and where their outputs get routed.

The Four Agent Types Your Team Needs

The research agent handles incoming questions that require synthesizing information from multiple sources. Give it a question and a set of raw inputs, and it returns a structured answer with key findings and confidence levels. This replaces the three-hour rabbit hole of manually reading everything and writing a summary from scratch.

The drafting agent handles first versions of recurring written outputs: meeting summaries, project updates, proposal outlines, brief announcements. It takes a set of bullet points and a tone guideline, and returns a clean draft ready for light editing.

The review agent checks drafts for clarity, completeness, and consistency against a standard. Give it a piece of writing and the standard it should meet, and it returns specific, actionable feedback. This is especially useful for teams that produce a lot of external-facing content.

The routing agent classifies incoming tasks and assigns them to the right agent or the right person. Give it a task description and a list of agent capabilities, and it returns a routing decision with a brief rationale.

Building Your Control Room

Start with the drafting agent and the research agent. These two cover the highest volume of recurring work for most teams. Create a shared prompt library document with the agent name, its role description, its input format, and its output format. Have everyone on the team use the same prompts for the same task categories. Consistency is what turns a collection of AI experiments into a reliable system.

Once the two core agents are running smoothly, add the review agent for your highest-stakes output type. After that, the routing agent becomes useful once you have enough agent types that people need help deciding which one to use. Build incrementally and only add complexity when the simpler version is already working.

import anthropic

client = anthropic.Anthropic()

AGENT_ROLES = {
    "research": "You are a research agent. Synthesize the provided material into a structured answer. Be specific, cite sources from the input, and flag uncertainty clearly.",
    "draft": "You are a drafting agent. Produce a clean, polished first draft based on the bullet points provided. Match the tone specified.",
    "review": "You are a review agent. Check the provided draft against the given standard. Return specific, actionable feedback only.",
    "route": "You are a routing agent. Given a task and a list of agent capabilities, return which agent should handle the task and why."
}

def run_agent(agent_type, task_input):
    response = client.messages.create(
        model="claude-opus-4-5",
        max_tokens=1024,
        system=AGENT_ROLES[agent_type],
        messages=[{"role": "user", "content": task_input}]
    )
    return response.content[0].text