Multi-Agent System Build (LangGraph, CrewAI, AutoGen)
Coordinated agent systems built on LangGraph, CrewAI, and AutoGen.
What we deliver
We design multi-agent systems that split complex work across specialized agents, with state, routing, and review built in from day one.
Some work is too complex for a single agent. We build multi-agent systems that divide a task across specialized agents and coordinate them with explicit state, routing, and review. We use frameworks like LangGraph, CrewAI, and AutoGen and pick the one that fits your problem, your team, and your runtime. We start by modeling the workflow as a graph of roles, decisions, and handoffs. Each agent gets a clear job, the tools it needs, and a contract for inputs and outputs. We add a controller that routes work, handles retries, and escalates to a human when confidence is low. We instrument the whole system so you can see what each agent did, why, and at what cost. The outcome is a transparent, debuggable system that handles work no single agent could complete on its own.
Built for teams like yours
Who it's for
- Teams running multi-step research or analysis
- Companies automating end-to-end workflows
- Product teams adding agentic features
- Operations leaders tackling complex processes
- Data and AI teams scaling pilots to production
Pain points we solve
- Single agent prompts that grow unmanageable
- Workflows that need different skills at different steps
- No visibility into why an agent made a choice
- Hard to debug failures across long runs
- Difficulty handing off between AI and humans
Capabilities
Everything we cover in this engagement.
- Workflow and graph design
- Framework selection (LangGraph, CrewAI, AutoGen)
- Agent role and tool definition
- Shared state and memory design
- Routing, retries, and escalation logic
- Human-in-the-loop checkpoints
- Tracing and cost observability
- Evaluation across full runs
Our process
A clear, predictable path from kickoff to outcomes.
Map
Model the workflow as roles, states, and handoffs.
Select
Choose the framework and architecture that fit the problem.
Build
Implement agents, tools, state, and the controller.
Evaluate
Run end-to-end tests on real cases and tune behavior.
Operate
Deploy with tracing, alerts, and a tuning cadence.
Deliverables & outcomes
What you get
- Multi-agent system in production
- Workflow graph and architecture docs
- Agent role definitions and prompts
- Shared state and memory layer
- Tracing dashboard and cost reports
- Runbook for operators
Outcomes you can expect
- Complex workflows handled end to end
- Clear visibility into each step and decision
- Lower failure rates through structured handoffs
- Easier debugging and iteration
- Predictable cost per completed workflow
What clients say
We were paying three agencies and a lifecycle freelancer to argue over attribution. RevoraOps absorbed all of it in 30 days, killed our worst-performing Meta ad sets, and rebuilt the welcome flow from scratch. CAC dropped 31 percent in the first full month. Honestly the relief of having one weekly call instead of four was worth it alone.
Our LCP was 4.8 seconds and Google was punishing us for it. They audited the build, dumped two plugins we did not need, moved hero images to a real CDN, and rewrote the critical CSS. LCP came down to 1.6 seconds within three weeks. Bounce rate on the pricing page dropped by a quarter without us touching the copy.
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ExploreFrequently asked questions
Quick answers to the questions we hear most.
When do I need a multi-agent system instead of one agent?
Which framework do you recommend?
How do you keep costs under control?
Can humans step in mid-workflow?
Will the system run on our cloud?
Have a workflow too complex for one agent?
We design and build coordinated multi-agent systems that handle the whole job.