Software Engineer → AI Agent Engineer

What makes this role different

AI Agent Engineers build systems where a model decides what to do next: calling tools, reading results, retrying, escalating and finishing tasks. That makes the engineering problems closer to distributed systems and workflow orchestration than to classic request/response software — with nondeterminism added on top.

Skills that transfer directly

• → Distributed systems experience maps directly to agent orchestration
• → Integration work (APIs, webhooks, auth) is what agent tool-calling actually is
• → Queues, retries, idempotency and state management are core agent-runtime problems

What you need to learn

• → Tool calling and function calling protocols, including MCP (Model Context Protocol)
• → Agent frameworks: LangGraph, OpenAI Agents SDK, Claude Agent SDK, and when not to use a framework
• → Agent evaluation: trajectory scoring, task success rates, cost per task
• → Sandboxing and permissions: what an agent is allowed to do, and how to enforce it
• → Memory and context management across long-running tasks

Projects that get interviews

• → Build an agent that completes a real multi-step task (e.g. triage and act on inbound requests) with a full audit log
• → Expose an MCP server for a real tool and document the design decisions
• → Write up an agent failure analysis: what went wrong, why, and how the harness caught it

Common mistakes

• → Building a demo chain of prompts and calling it an agent — reliability and state handling are what employers probe
• → Ignoring evaluation and observability; production agents fail in ways you must be able to see
• → Overusing frameworks; interviewers often prefer candidates who understand the primitives