Chat with us!We engineered a multi-agent AI system where specialized agents — research, analysis, writing, and review — collaborate under an orchestrator to complete co...
It is like having a small expert team that researches, drafts, and checks its own work — at machine speed.
Chief Technology Officer
We engineered a multi-agent AI system where specialized agents — research, analysis, writing, and review — collaborate under an orchestrator to complete complex tasks that a single model could not reliably handle alone.
| Project Detail | Information |
|---|---|
| Project Type | Web |
| Industry | Technology |
| Technologies | LangGraph, LLMs (mixed), Python, Redis, PostgreSQL, OpenTelemetry |
| Delivery Partner | mTouch Labs |
| Primary Outcome | The multi-agent system produced markedly higher-quality, more reliable output on complex tasks, with full transparency into every step. |
The client needed to automate end-to-end knowledge work that spanned research, synthesis, and quality control. A single prompt-and-response model produced shallow, inconsistent output and could not self-correct.
We decomposed the task into roles, each handled by a specialized agent with its own tools and prompt, coordinated by an orchestrator that manages state, delegation, and a critique-and-revise loop.
The system runs a planner, domain agents, and a reviewer agent in a shared workspace, with full step-level tracing so every decision is observable and debuggable.
Multi-agent orchestration and state
Role-specialized reasoning and generation
Agent runtime and tool layer
Shared agent memory and queues
Run history and artifacts
Step-level tracing and observability
Broke the target workflow into agent roles and interfaces.
Built the planner, delegation, and shared-state model.
Tuned each agent's prompt and tools for its role.
Added a reviewer agent and revision cycle for quality.
Instrumented every step and added failure recovery.
The multi-agent system produced markedly higher-quality, more reliable output on complex tasks, with full transparency into every step.
Coordinated, specialized agents with a review loop unlocked reliable automation of complex knowledge work that single-model approaches could not deliver.
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View case studyWhat businesses ask us most often about this project and how we built it.
It is an architecture where multiple specialized AI agents collaborate — under an orchestrator — to plan, execute, and review complex tasks that exceed what a single model can do reliably.
Specialized agents with focused roles and a critique-and-revise loop produce deeper, more consistent results and can catch and fix their own mistakes.
Yes. Every step is traced and replayable, so you can see exactly how each result was produced and debug failures.
Agents are pluggable, so new roles and tools can be added without rebuilding the orchestration.
We assign the right-sized model to each role and cache shared context, balancing quality against cost and speed.
mTouch Labs combines AI-powered development with deep industry expertise to deliver solutions faster.
