Multi-agent system development
When one agent tries to do everything, it gets slow, expensive and hard to debug. We split the work across specialised agents with clear jobs, coordinate them explicitly, and keep a person in charge of anything consequential.
MappOptimist designs and builds multi-agent AI systems: specialised agents for distinct jobs, coordinated by a planner or an explicit state graph in LangGraph, sharing governed tools that can be exposed through MCP servers, with human approval gates, tracing and evaluation on every hand-off. Our multi-agent support system for a logistics and e-commerce platform automated 65% of repetitive tasks and cut response times by 40%.
Problems we solve
One agent, too many jobs
A single agent with dozens of tools and a long prompt starts picking the wrong tool, forgetting instructions and costing more per request. Every new task makes the others worse.
Hand-offs nobody can follow
Agents passing work to each other through free text create loops and dead ends. When something fails, there is no record of which agent decided what, or why.
Each team wires up its own tools
Support, operations and finance build separate integrations to the same systems, each with its own permissions and bugs. Nothing is reused and nothing is governed in one place.
Autonomy without control
Agents that can refund, update records or message customers need approval rules, limits and an audit trail. Most frameworks leave that to you.
What we build
Agent decomposition and design
We split the workflow into agents with one clear job each, such as tracking, support and payments, and define what each may read, which tools it may call and when it must hand off or escalate.
Orchestration with explicit state
A supervisor agent or a state graph in LangGraph routes work between agents, so every step, branch and hand-off is explicit, testable and replayable rather than buried in prompts.
Shared, governed tools over MCP
Each tool is a typed, permissioned wrapper around your API or database. Tools can be served through MCP servers, so several agents share one governed interface instead of each team building its own.
Memory and context across agents
Session memory for each user, shared context that agents can read and write deliberately, and retrieval from your documents and live systems, so agents work from the same facts.
Human approval and escalation
Consequential actions pause for a person to approve, and low-confidence or sensitive cases go to a human with the full context of what each agent did.
Tracing, evaluation and cost control
Every plan, hand-off and tool call is traced. A scenario suite tests the whole system before release, and model routing and limits keep latency and token spend predictable.
How an engagement runs
- Step 01
Map the workflow
We walk through the work as it is done today, mark where decisions and approvals happen, and agree which parts agents should take on first.
- Step 02
Design the agents and tools
Agent roles, the orchestration graph, the tool registry with permissions, memory and approval gates, plus an evaluation set built from real cases.
- Step 03
Build one path end to end
One workflow is built across all the agents it needs, with tracing and approvals working, and tested against the evaluation set before real users see it.
- Step 04
Expand and operate
More workflows reuse the same tools and agents. We monitor quality, latency and cost in production and tune the system as usage grows.
Ways to work with us
Multi-agent build
We scope and deliver the system end to end, or run a managed pod against outcomes you set, with milestones and a production-ready handover.
Dedicated AI team
Agentic systems developers, LLM engineers and an AI solutions architect who work in your standups and own the agent roadmap alongside your team.
Individual experts
An agentic systems developer or LLM engineer who joins your team on a monthly rolling basis. You interview and approve everyone before they start.
Need individual engineers rather than a project? See the roles you can hire and their rates.
Technology we work with
Multi-agent systems we have built
Autonomous support agents
A multi-agent customer support chatbot — specialised bots for tracking, support and payments — that automates repetitive queries and frees the team for complex issues.
Read the case study →AI Agents · EnterpriseAgentic AI Platform
Goal-driven AI agents that plan, call tools and complete real workflows under human oversight.
Read the case study →AI Agents · HealthcareHospital Management Agentic System
AI agents that coordinate hospital operations — scheduling, records and workflows — under staff control.
Read the case study →Frequently asked questions
When is a multi-agent system better than a single agent?
When the work has distinct jobs that need different tools, data or rules, such as tracking, support and payments. Splitting them keeps each agent's instructions short and its tools few, which makes it more accurate, cheaper per request and easier to test. For a simple task with a handful of tools, one well-built agent is usually enough.
Which framework do you use: LangGraph, CrewAI or AutoGen?
Our default is LangGraph and LangChain, because explicit state graphs make every hand-off visible, testable and replayable, which matters once agents act inside business systems. CrewAI and AutoGen are reasonable alternatives for some designs; if your team already uses one, we review it with you before recommending a change.
What is MCP and why use it?
The Model Context Protocol is an open standard for exposing tools and data to AI models. Serving your tools through MCP servers means several agents, and several teams, share one governed, permissioned interface to each system instead of building separate integrations.
How do you keep agents from making harmful decisions?
Agents can only act through permissioned tools with typed inputs. Consequential actions pause for human approval, sensitive or low-confidence cases escalate to a person with full context, and every step is traced so it can be reviewed and, where needed, reversed. Our agent systems for an enterprise SaaS company in the UK and a hospital network in the US both run this way.
What results have your multi-agent systems delivered?
For a logistics and e-commerce enablement platform in India, specialised agents for tracking, support and payments automated 65% of repetitive tasks, cut response times by 40% and increased customer satisfaction by 30%. Our other agent case studies describe outcomes qualitatively, because their source documents do not state figures.
What does it cost to build a multi-agent system?
Cost depends on the number of agents and systems involved, approval flows, evaluation depth and expected traffic. Builds are quoted after scoping. For team augmentation, our 2026 rate card lists an Agentic Systems Developer at $38, $45 or $52 per hour and an AI Solutions Architect at $44, $52 or $60 by experience band. Rates are indicative and confirmed per engagement.
Design your agent system
Tell us the workflow, the systems involved and where a person must stay in control. We reply within one business day with how we would split the work across agents.