How it works
Request → approved context → reasoning and rules → approved tools → human approval when needed → action or answer → logs and measurement
Custom AI agents
Custom AI agents built around your business workflows
CombineApps designs AI agents that read relevant context, follow agreed rules, use approved tools, and complete multi-step work. When a decision should stay with a person, the workflow pauses for human approval.
Best fit
A custom agent is most useful when the work repeats but cannot be expressed as fixed rules alone. The workflow should have a clear owner, accessible context, defined actions, and a measurable outcome.
Example workflows
Where this capability can help
These are examples, not claimed customer results. The right design depends on your workflow and systems.
Qualify and route inbound leads, draft follow-ups, and prepare CRM updates.
Answer common support questions and escalate complex cases with useful context.
Find answers across approved SOPs, documents, product information, and internal knowledge.
Process routine requests, update systems, and prepare scheduled summaries.
Coordinate specialized agents for research, drafting, review, and routing steps.
Typical deliverables
- Current-state workflow map and solution blueprint
- A focused agent or coordinated multi-agent workflow
- Connections to approved data and business tools
- Representative evaluation cases and acceptance criteria
- Deployment, logging, monitoring, and failure handling
- User documentation, handoff, and an agreed support plan
Controls designed with the build
- Least-privilege access to data and actions
- Human approval for sensitive or irreversible steps
- Validation, timeouts, retries, and stop conditions
- Escalation paths for uncertainty and exceptions
- Logs and review samples tied to agreed metrics
Common questions
Direct answers before discovery
What is a custom AI agent for business?
It is a software system designed for a defined workflow. It can interpret approved context, follow instructions, use a limited set of tools, and produce an answer or action within explicit permissions.
Does an AI agent have to act without human approval?
No. An agent can stay read-only, prepare drafts, request approval before an action, or operate automatically only for low-risk cases. Authority should match the consequence of a mistake.
How do you evaluate an AI agent before launch?
We define acceptance criteria and test representative normal, edge, ambiguous, and refusal cases. The evaluation should cover the specific output, tool use, escalation, and safety behavior the workflow requires.
Feasibility first
Final feasibility depends on representative data, integration access, permissions, exceptions, and the actions the agent is allowed to take. If a deterministic automation is simpler and more reliable, we will recommend that instead.