Multi-Agent Systems for Complex Enterprise Workflows
Some workflows are too large or varied for a single AI agent. We design systems where specialized agents take defined roles, hand work to each other and escalate to people at agreed points.
When One Agent Is Not Enough
Multi-agent designs add complexity, so we use them only when the workflow needs them.
Several kinds of work
The workflow mixes research, drafting, checking and system updates that suit different roles.
Several systems
Work moves across CRM, ERP, documents and email, each with its own access rules.
Checks and balances
You want one agent to check another before anything reaches a customer or a ledger.
Volume and parallel work
Many cases need handling at once, with consistent quality.
What We Build
A set of agents with clear roles, an orchestration layer that coordinates them, and controls that keep people in charge.
Role-based agents
Each agent has a defined job, instructions, tools and permissions, which makes it easier to test and change.
Orchestration
A coordinating layer that assigns work, shares context and manages hand-offs.
Review agents
Agents that check other agents' outputs against rules before they move on.
Escalation paths
Clear rules for when a case goes to a person, and to whom.
Shared memory
Context shared between agents only where the workflow needs it.
Observability
A single view of what each agent did and why.
How It Works
Map the workflow
Identify the roles, decisions and hand-offs in the current process.
Design the agents
Define each agent's role, tools and permissions.
Design orchestration
Decide how work moves between agents and to people.
Pilot in a sandbox
Test the system against real cases and agreed success criteria.
Deploy in stages
Go live in a bounded scope, then expand with monitoring.
Where People Stay in Control
Human approval
Consequential outputs are approved by a person before they take effect.
Per-agent permissions
Each agent has its own least-privilege access.
Conflict rules
When agents disagree, a defined rule or a person decides.
Emergency stop
Any agent or the whole system can be paused and rolled back.
What We Measure
Measures are agreed during the readiness diagnostic and reported against your baseline.
End-to-end cycle time
Time from case opened to case closed.
Quality
Errors caught by review agents and by people.
Escalation rate
How often cases need a person, and why.
Cost per case
Model, system and staff cost per completed case.
Frequently Asked Questions
What is a multi-agent system?
A set of specialized AI agents that work together on one workflow, coordinated by an orchestration layer.
Is it better than a single agent?
Only when the workflow needs several roles. For simpler workflows, one agent is cheaper and easier to run.
Which frameworks do you use?
We choose per project from established frameworks such as LangGraph, CrewAI and AutoGen, or build lightweight orchestration where that is simpler.
How do you keep it safe?
Per-agent permissions, human approval at consequential steps, conflict rules and full logging.
Related
AI Sales & Proposal Agent (use case)
An illustrative workflow with research, drafting and review roles.
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