Project 7 — Multi-agent customer support
Words made simple
A few useful meanings before you read the explanation.
- Agent
- An application that uses a model to choose its next action within defined limits. It can use tools and continue through several steps.
- AI
- Software techniques for tasks such as recognizing patterns, understanding language or generating content.
- API
- A way for one piece of software to ask another for data or an action. A claims API might return the current status of a claim.
More words from this lesson
- Schema
- A description of the fields and value types that data should contain. It helps check that an answer has the expected shape.
- State
- The data describing where a workflow is now, what it already knows, and what still needs to happen.
- Evaluation
- Checking results against examples and clear criteria to find whether the system works well and where it fails.
- Observability
- Being able to understand system behavior through records such as logs, measurements and traces.
- Latency
- How long a request takes to return a result. Lower latency usually means a shorter wait for the user.
Requirements
Add specialists only for measurable policy/claim/document tasks, with clear coordinator ownership and escalation.
Architecture
Supervisor → scoped specialist subgraphs → typed reports → consistency check → response. Keep final writes outside specialist discretion.
Technologies
Existing graph/SDK, typed contracts, shared run budgets, scoped tools; reuse one infrastructure stack.
Database design
Parent/child run IDs, report evidence references, role/version tags, and conflict/escalation state.
API design
Public conversation/run API stays stable; specialist endpoints/contracts are internal and bounded.
AI design
Minimal per-role context, independent completion criteria, limited delegation depth, and explicit conflict handling.
Security
Distinct tool permissions; no automatic inheritance of broader supervisor credentials; controlled context sharing.
Evaluation
Compare against one agent on task success, support quality, cost, latency, and permission failures. Test contradictory specialists.
Observability
Delegation graph, role calls, shared budget, partial failures, and final ownership.
Deployment
Scale independent workers only when justified; preserve compatible report schemas.
Interview questions
- “Why multiple agents?” Answer: specialization must prove a benefit.
- “What if they disagree?” Answer: inspect evidence and apply authoritative rules or escalate; agreement alone is not truth.
Acceptance: Demonstrable benefit over the single-agent baseline with bounded coordination cost.
Can you explain it without the notes?
Project 7 — Multi-agent customer support
Use your own words. Explain an example and one limitation before checking the explanation.
Check your explanation
Requirements: Add specialists only for measurable policy/claim/document tasks, with clear coordinator ownership and escalation.
Turn this concept into a clear answer.
Practice this conceptAdapted from 13_AI.MD · “Project 7 — Multi-agent customer support”. Examples are study fragments; framework behavior can depend on your version.