01
Constrain the agent scope
Define exactly what the agent can decide, what tools it can call, and when it must ask for review.
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Agentic AI workflows
Design AI agents that can execute useful business tasks safely
Create agentic workflows that reason across steps, call tools, coordinate handoffs, and keep humans in control when judgment is needed.
Best strategies
What makes this use case work
02
Use typed tool contracts
Give agents predictable APIs with validation, permissions, and clear success or failure states.
03
Operate with observability
Capture traces, decisions, tool calls, approvals, costs, and outcomes so teams can improve the workflow over time.
Showcase example
Showcase example: support operations agent
Scenario
A support team uses an agent to classify requests, search knowledge, draft responses, and create follow-up tasks.
Outcome
Response time improves while sensitive customer actions still require human approval.
End-to-end process
How we move from strategy to production
Phase 01
Select a repeatable workflow with known inputs, decisions, and handoffs.
Phase 02
Define tools, permissions, policies, memory, and approval points.
Phase 03
Prototype the agent with test tasks and failure scenarios.
Phase 04
Deploy with monitoring, escalation paths, and measured expansion.