Multi-Agent Business System
How specialized agents communicate and work together instead of one model trying to do everything.
Design study · Concept
Give each agent a clear job—and make the handoffs explicit.
This concept explores how a business could coordinate specialized agents instead of expecting one assistant to handle every function. A routing layer delegates work, passes structured results between roles, and brings the output back for review.
Scope each responsibility
Each proposed agent has a defined function, context, and tools. This is intended to make ownership and review clearer as workflows grow.
Design the handoff
The concept uses structured results between stages so the next agent receives the information it needs. Missing or ambiguous input should be surfaced for review.
Evaluate the whole workflow
The relevant question is whether the coordinated system completes a business task reliably—not simply whether each agent can generate a response.
What this demonstrates
This is a proposed architecture, not a deployed customer system. It illustrates the approach Aether would evaluate before implementation.
Measuring effectiveness
A future prototype should compare task completion, handoff errors, human corrections, and cost with a simpler single-agent baseline. No benchmark has been run for this concept.
Problem
A single general-purpose AI assistant tends to do every job a little worse than a specialist would — and gets harder to trust as its scope grows.
Solution
A set of function-scoped agents, each with a defined responsibility and its own working memory, coordinated so the right agent handles the right job and hands off cleanly to the next one.
Architecture
Each agent owns a distinct business function with its own context and tools. A coordination layer routes work to the right agent and passes structured results between them, rather than one model juggling every responsibility in a single context.
Workflow
Technology
Screenshots
Screenshots in progress
This system is still being built — screenshots will be added as it takes shape.
Demo
Demo in progress
A walkthrough of this project isn't recorded yet — check back soon.
Lessons
- Scoping an agent to one function makes it dramatically easier to trust and to debug than a single do-everything assistant.
- Clean hand-offs between agents matter as much as any individual agent's quality.
Potential Business Applications
- Businesses running several distinct AI-assisted workflows (support, ops, marketing, finance) that currently live in separate disconnected tools
- Teams that want visibility into what each agent is responsible for, rather than one opaque assistant