Design AI workflows that can use tools, maintain state, request approval, fail safely, and remain observable.
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Agentic AI Engineers build controlled software workflows in which models can reason over context, call approved tools, maintain state, and operate within explicit permissions and human-approval boundaries.
Percentages show how often each skill appears across relevant current opportunities for this career.
Build reliable services, tests, interfaces, and integrations.
Expose business capabilities through bounded, validated tools.
Separate workflow state from long-term memory and control retention.
Choose simple deterministic flow where possible and agentic control where justified.
Limit what tools, data, identities, and actions an agent can access.
Measure trajectories, tool choices, outputs, and failure behavior.
Trace model decisions, tool calls, latency, errors, and approvals.
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Agent services, integrations, evaluation, and orchestration.
Controlled access to business systems.
Standardized tool/resource connectivity where appropriate.
Optional orchestration after workflow and security design.
Inspect model decisions, tool calls, and failures.
Version, test, and review agent changes.
Start with the business process, users, boundaries, and success criteria.
Turn external capabilities into validated contracts with least privilege.
Use the smallest reliable pattern that can complete the workflow.
Require human review for high-impact actions and design safe fallback.
Test trajectories and monitor behavior in production.
Strengthen Python, HTTP, JSON, testing, authentication, and Git.
Model tasks as bounded tools and explicit workflows.
Design state, context, memory, and control flow.
Add least privilege, human gates, adversarial tests, and trajectory evaluation.
Trace, monitor, version, and improve agent workflows.
Service workflows require multiple systems and decisions, but unrestricted automation would create operational risk.
Build an agentic workflow that acts only within defined tools, permissions, and approval gates.
Design a controlled service-workflow agent that gathers case context, calls approved tools, requests human approval for sensitive actions, and records evidence.
Users, tools, state, data, approvals, and prohibited actions.
Validated schemas, authentication, permissions, and error behavior.
Actions allowed automatically versus actions requiring human review.
Normal, adversarial, tool-failure, and permission-boundary scenarios.
Evidence showing tool choices, state transitions, and failure handling.
Shows workflow-first design.
Demonstrates integration and security boundaries.
Shows the ability to test agent trajectories.
Demonstrates human-control design.
Shows operational maturity.
Discuss uncertainty, tool choice, branching, cost, and reliability.
Cover identity, least privilege, validation, approvals, and audit.
Discuss trajectories, tool choices, outcomes, failures, and regression sets.
