Agentic Workflow Automation
Multi agent systems with clear role boundaries
A multi agent system splits work across several specialized agents that hand off to one another under an explicit coordinator. Lingows AI builds these systems where a single agent's scope would be too broad, and keeps every handoff logged so a failure can be traced to the step that produced it.
01What this is
Multiple agents are justified when roles genuinely differ, for example an extraction agent, a validation agent, and a drafting agent. Each has a narrow scope, its own tools, and its own evaluation set.
Coordination is the hard part. Lingows AI uses an explicit orchestrator with defined state rather than letting agents converse freely, because free conversation between agents is difficult to debug, expensive, and prone to loops.
02Who this suits
Genuinely distinct roles
Work where different steps need different tools, contexts, and success criteria.
Long running processes
Workflows that span hours or days and must survive restarts.
Teams with a working single agent
Organizations extending a proven agent rather than starting with complexity.
03How it works
- Step 01
Justify each agent
An agent is added only when a role needs different tools or a different evaluation set.
- Step 02
Use an explicit orchestrator
State transitions are code. Agents do not negotiate with each other about what happens next.
- Step 03
Evaluate per agent
Each role holds its own regression set so a failure is attributed to a step, not to the system as a whole.
04Effort and timeline
| Item | Typical |
|---|---|
| Typical build | 8 to 16 weeks |
| Agent count | 2 to 5 in a first system |
| Coordination | Explicit orchestrator with durable state |
| Debugging | Per step traces retained |
05Common failure modes
Agents for the sake of agents
Splitting one clear job across three agents multiplies cost and latency and improves nothing.
Free form agent chat
Unstructured negotiation between agents produces loops that are expensive and hard to reproduce.
Shared mutable context
When every agent writes to one blob of state, no failure can be localized.
FAQQuestions
Common questions
When is one agent enough?
Most of the time. Lingows AI recommends a single agent until a specific role clearly needs different tools, a different context, or a separate evaluation standard.
How is cost controlled across many agents?
By routing cheaper models to narrow steps, keeping context small per role, and tracking spend per agent so a single expensive step is visible.
Last reviewed 2026-08-16
UPWhere this sits
Multi Agent Systems is part of Agentic Workflow Automation. The pillar page covers scope, delivery model, and the other capabilities in the set.
- Human in the Loop
Human in the loop means a person approves, edits, or rejects an AI output before it takes effect. Lingows AI places those gates where the cost of an error is highest, builds review queues that take seconds rather than minutes, and measures every correction so the gate can eventually move.
- n8n Orchestration
n8n is a workflow automation platform that connects systems through visual nodes and can host AI steps inside a larger process. Lingows AI builds n8n workflows for production use, which means error branches, idempotent retries, credential separation, version control, and monitoring rather than a working happy path.
- Workflow Design
Workflow design is the mapping step that happens before any automation is built. Lingows AI documents how a process runs today, separates the steps that need judgment from the steps that are rules, places approval gates by error cost, and produces a specification an engineering team can build against.
Talk through multi agent systems
Bring the process, the volume, and the systems it touches. Lingows AI will scope the build, the effort range, and what running it costs.