AI copilots and agents in B2B software
AI copilots and agents add model-driven help to business software: answering questions over company data, drafting documents, classifying records and running multi-step tasks.
Most B2B products add them to existing workflows, which raises questions about review, authorship and audit.
Key facts
- Three common modes: chat, inline suggestions and background agents.
- They work over company records, documents and code.
- Review happens before or after execution, depending on the action.
- The product has to record which values a model produced.
- General-purpose assistants set users’ expectations.
What it covers
| Aspect | In this category |
|---|---|
| Modes | Chat, inline suggestions, background agents |
| Data | Company records, documents, code |
| Control | Review before or after execution |
| Audit | Which values a model produced |
| Expectations | Set by general-purpose assistants |
Modes compared
| Mode | Where it appears | What the user controls |
|---|---|---|
| Chat | A separate panel or page | The question and whether to use the answer |
| Inline suggestion | Inside a form or record | Accept, edit or reject each suggestion |
| Background agent | Runs without the user present | What it may do, and review of the result |
What makes it hard to design
Scope. Each mode needs a clear scope, so users know what it can and cannot touch.
Sources. Showing sources with every output lets users check an answer against company data.
Approval. Approval steps sized to the consequence of the action keep low-risk work fast and high-risk work reviewed.
Authorship. Model authorship stored with each value lets an audit trail separate what a person entered from what a model produced.
Structured modes give better results for specific tasks, and users who cannot see what they do stay in chat.
Trade-offs
- Review before execution can catch errors but adds effort and is not a guarantee of safety. Post-action controls suit only actions whose consequences, monitoring and recovery have been assessed.
- A single chat entry point is easy to find and hides what the product can do. Several modes show more of the product and make users choose before they start.
Common mistakes
- One approval step for every action. Users approve without reading when low-risk and high-risk actions look the same.
- Outputs without sources
- Model-produced values stored as if a person entered them
- Structured modes hidden behind a chat box
Where we’ve worked on this