Agent Overview: Your Guide to the Open Deep Research Multi-Agent System in Claude Code

Why This Agent Exists — and Why It Saves You Hours

Complex research projects don’t fail because developers lack intelligence. They fail because orchestrating multi-step, multi-source investigations manually is genuinely hard. You lose track of what’s been covered, duplicate effort across threads, miss contradicting sources, and end up with a flat summary instead of rigorous analysis.

The Agent Overview agent solves a meta-problem: before you can use a sophisticated nine-agent research system effectively, you need a clear map of what each agent does, when it activates, how it hands off to the next stage, and what the entire workflow looks like from query to final report. This agent is that map. It’s the documentation layer baked directly into your Claude Code environment — a reference you can query conversationally while you’re building, debugging, or extending the Open Deep Research Team.

Senior developers working with multi-agent systems spend a disproportionate amount of time re-reading documentation, reverse-engineering prompt chains, and figuring out which agent to invoke for which task. This agent eliminates that friction by giving you a queryable, always-available overview of the entire research team architecture — its agents, their responsibilities, their inputs and outputs, and the orchestration logic that ties them together.

When to Use the Agent Overview Agent

This agent belongs in your workflow in several specific situations:

• Onboarding to the research team system: When you or a team member is new to the Open Deep Research Team and needs to understand the full agent hierarchy before writing any code or prompts.
• Debugging a failing research workflow: When a research pipeline breaks mid-execution and you need to quickly identify which agent is responsible for the failing stage — query clarification, brief generation, coordination, or report synthesis.
• Extending the system with a new specialist: When you’re adding a tenth agent and need to understand where it fits in the dependency chain and how it should receive and emit structured data.
• Reviewing agent boundaries during code review: When validating that a new feature properly respects the separation of concerns between, say, the Research Orchestrator and the Research Coordinator.
• Writing integration tests: When you need precise descriptions of each agent’s expected inputs, outputs, and quality gates to write meaningful assertions.
• Explaining the architecture to stakeholders: When a non-technical project lead needs to understand what “multi-agent research” actually means in concrete terms without reading nine separate system prompts.

Real-World Examples

Scenario 1: A developer joins mid-project and needs a fast ramp-up

A backend engineer has just been added to a project using the Open Deep Research Team. They need to understand the system well enough to fix a bug in the query processing stage by end of day.

Developer: "Give me a quick breakdown of what each agent does and the order they run in."

Agent Overview: "The research workflow runs through nine agents in a hierarchical sequence. 
It starts with the Research Orchestrator, which manages the entire workflow and routes tasks. 
From there, the Query Clarifier analyzes the incoming query for ambiguity and assigns a 
confidence score between 0.0 and 1.0. If the score is low, it generates structured 
clarification questions before proceeding. Once the query is clean, the Research Brief 
Generator transforms it into a structured plan with specific research questions, keywords, 
and source preferences. The Research Coordinator then takes that brief and allocates tasks 
across specialist researcher agents, managing parallel threads and dependencies. Downstream 
agents handle source retrieval, synthesis, fact-checking, and final report generation. 
Where specifically is the bug manifesting?"

Developer: "The system is skipping clarification even on vague queries."

Agent Overview: "That points to the Query Clarifier's confidence scoring logic. It uses 
a 0.0 to 1.0 scale — if the threshold is set too high, even ambiguous queries pass 
through without triggering clarification questions. Check the decision framework 
configuration in that agent's system prompt or the threshold parameter in your 
orchestration layer."

Scenario 2: Extending the system with a new agent

A developer wants to add a Bias Detection Agent that flags one-sided sourcing in research outputs.

Developer: "I want to add a bias detection step. Where does it fit in the pipeline?"

Agent Overview: "Based on the existing architecture, bias detection makes most sense 
as a quality gate after the synthesis stage but before the final report is handed to 
the Research Orchestrator for delivery. At that point, all sources have been aggregated 
and cross-referenced, giving your new agent a complete dataset to analyze for sourcing 
imbalances. It should emit a structured JSON result — similar to how the Query Clarifier 
outputs confidence scores — so the Orchestrator can decide whether to flag the report, 
trigger additional research from underrepresented perspectives, or pass it through. 
Do you want me to outline the input/output contract it should follow to match the 
existing agent interfaces?"

Scenario 3: Writing a test suite for the research pipeline

Developer: "What should I assert in an integration test for the Research Brief Generator?"

Agent Overview: "The Research Brief Generator takes a clarified query and produces a 
structured research plan. Your assertions should verify that the output includes: 
at minimum one specific research question derived from the query, a keyword list 
for targeted searching, identified source types or preferences, defined success 
criteria, and scope boundaries. You should also test that it handles both narrow 
queries — where the brief should be tightly scoped — and broad queries — where it 
should decompose the topic into multiple sub-questions. The output format is designed 
for seamless handoff to the Research Coordinator, so assert that the data structure 
matches what that downstream agent expects to receive."

What Makes This Agent Powerful

Architectural clarity at query time

Rather than digging through documentation files or reading nine separate system prompts, you get a single conversational interface that understands the relationships between agents — not just their individual descriptions. Ask about handoffs, dependencies, quality gates, or error handling, and get answers grounded in the full system architecture.

Precise agent boundary definitions

Multi-agent systems break when responsibilities blur. The Agent Overview agent enforces conceptual clarity around what each agent owns. The Research Orchestrator manages workflow state. The Query Clarifier owns ambiguity detection. The Research Brief Generator owns planning. The Research Coordinator owns task allocation. Understanding these boundaries precisely is what lets you debug, extend, and test the system without introducing regressions.

JSON-aware output modeling

Several agents in the research team use structured JSON output for inter-agent communication — the Query Clarifier’s confidence scores, the Research Brief Generator’s plan objects, coordination state from the Research Coordinator. The Agent Overview agent understands this data contract layer and can help you reason about integration points when building new agents or debugging serialization issues.

TodoWrite integration awareness

The Research Orchestrator uses TodoWrite for transparent progress tracking. The Agent Overview agent understands this mechanism, which means you can ask it to explain how in-flight research state is represented and recovered — useful when you’re debugging interrupted research sessions or building persistence layers on top of the system.

Workflow phase sequencing

The system runs in distinct phases with quality gates between them. The Agent Overview agent knows these gates — where validation happens, what triggers re-routing, what constitutes graceful degradation. This is the kind of operational knowledge that usually lives only in the head of whoever wrote the system. Here, it’s queryable.

How to Install the Agent Overview Agent

Claude Code supports sub-agents defined as Markdown files in your project’s .claude/agents/ directory. When Claude Code loads, it automatically discovers and activates any agent files it finds there. Installing the Agent Overview agent takes about sixty seconds:

• In the root of your project, create the directory path .claude/agents/ if it doesn’t already exist.
• Inside that directory, create a new file named agent-overview.md.
• Paste the full Agent Overview system prompt into that file and save it.
• Restart or reload Claude Code — it will automatically detect the new agent and make it available in your session.

No configuration files, no registration steps, no environment variables. The file presence is sufficient. If you’re managing a team, commit this file to version control so every developer on the project has the same agent available from the moment they clone the repository.

If you’re running multiple agents from the Open Deep Research Team, each one gets its own Markdown file in .claude/agents/. The Agent Overview agent sits alongside the Orchestrator, Query Clarifier, Brief Generator, and the rest — giving you a queryable index of the entire system without cluttering your source tree.

Conclusion and Next Steps

The Open Deep Research Team is a sophisticated system. Sophisticated systems require clear internal documentation that developers can actually use in real time — not PDFs nobody reads, but conversational agents that answer precise questions during active development. The Agent Overview agent is that documentation layer.

If you’re starting fresh with the research team, install this agent first. Use it to orient yourself on the workflow, then bring up the individual specialist agents one by one. If you’re inheriting an existing implementation, query it against whatever part of the system is giving you trouble. If you’re extending the system, use it to validate your architectural thinking before writing any prompts.

Concretely: create .claude/agents/agent-overview.md, paste in the system prompt, and run your first query against it today. The thirty seconds it takes to install will pay back in every debugging session, onboarding conversation, and architecture review you run going forward.

Agent template sourced from the claude-code-templates open source project (MIT License).