Multi-Agent Orchestration — The MentorMe Pattern

One agent is a chatbot. Four agents is a team. Four agents coordinating on a single task, without collisions or lost context, is an operating system.

That's the distance we've been closing at MentorMe for the past year, and the pattern we've landed on is worth publishing because most people in the space are still stuck at "one agent does one thing."

Here's the problem first. When you have more than one AI agent working on a task, three things break. They step on each other's work. They forget what the other agents did. They argue about whose job it is. If you've ever watched two agents rewrite the same file four times, you know the pain.

The MentorMe pattern is built around four roles — an orchestrator, a researcher, a builder, and a verifier. Each one has a narrow job. Each one has a memory boundary. And the orchestrator is the only one who sees the whole picture.

The orchestrator is the manager. It takes the user's request, breaks it into subtasks, decides which agent does what, and merges the results at the end. It does not do any of the actual work. That's important — when the orchestrator starts doing work, quality drops because it's splitting attention. Keep it managerial.

"One thing we've proven across hundreds of agent-hours at MentorMe — the bottleneck isn't the model."

The researcher is the scanner. It reads files, hits APIs, scrapes the web, pulls data from databases. It does not write anything new. Its only output is a structured brief — here's what I found, here's the source, here's the confidence level. The brief goes back to the orchestrator, not to the other agents directly. This matters. Agent-to-agent communication without a coordinator is where chaos lives.

The builder is the maker. Code, copy, designs, specs, whatever the task requires. The builder reads the researcher's brief and produces the artifact. But here's the key — the builder does not decide if the artifact is good. That's a separate job. A builder who also grades its own work will always give itself an A.

The verifier is the critic. It takes the builder's output and checks it against the original request. Does this match what the user asked for? Does it meet the quality bar? Are there bugs, broken links, missing sections, bad data? The verifier has permission to reject. If it rejects, the task goes back to the builder with specific feedback. If it passes, the orchestrator returns the result to the user.

This sounds slow. It isn't. In practice, on Claude Opus 4.7, a four-agent cycle runs in 20–90 seconds for most knowledge-work tasks. The extra time is paid back immediately in fewer regressions.

The hard part is context management. Each agent has a maximum context window. If the task is big, the raw data won't fit in any single agent's head. The trick is compression at each handoff. The researcher compresses findings into a brief. The orchestrator compresses the task into a prompt for the builder. The verifier receives only the builder's output and the original request — not the full research dump. Every handoff is a compression, and compression is where orchestration lives or dies.

Another piece that trips people up — shared memory. If each agent runs in isolation, they forget everything as soon as their turn ends. The fix is a shared scratchpad. We use a simple JSON file or a vector database that every agent reads at the start of their turn and writes to at the end. It's not elegant. It works. The alternative — passing the full chat history to every agent every turn — blows the context window and costs a fortune in tokens.

The failure modes are worth naming so you can spot them. Failure one is role bleed — the researcher starts writing code, the builder starts fact-checking, the verifier starts building. Role bleed means your prompts aren't strict enough. Rewrite them with explicit restrictions. Failure two is infinite verification — the verifier keeps rejecting, the builder keeps rewriting, the loop never ends. The fix is a retry cap. Three retries, then escalate to the human. Failure three is context drift — by turn five, the agents have forgotten what the original task was. The fix is re-injecting the original task into every prompt as a fixed preamble.

One thing we've proven across hundreds of agent-hours at MentorMe — the bottleneck isn't the model. Claude Opus 4.7, GPT-5.5, Gemini 3.1 Pro — all of them are smart enough. The bottleneck is the architecture around the model. A mediocre model with great orchestration beats a great model with bad orchestration. Every time.

Build a two-agent version of this pattern today — one researcher and one writer, sharing a scratchpad, coordinated by a simple prompt.

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