Agentic AI: Roadmap & Interview Preparation

Thinking in Agents

An agent is a system that perceives state, decides on an action, executes that action, and updates its understanding of the world — repeatedly, until a goal is satisfied or a stopping condition is reached. The LLM inside an agent is the decision engine, but the scaffolding around it determines whether the system is robust enough to trust in production.

Interviewers in 2026 are no longer asking whether you know what an agent is. They are asking how you would design one that handles partial failures, recovers from tool errors, avoids infinite loops, and produces auditable traces. This guide gives you a roadmap for building that depth of knowledge.

The Four Pillars of a Production Agent

• Planning: how the agent decomposes a goal into sub-tasks (ReAct, plan-and-execute, tree-of-thought)
• Memory: in-context window, external vector store, structured key-value state
• Tool use: schema design, error handling, idempotency, and rate limits
• Evaluation: task completion rate, step efficiency, and safety metrics

Multi-agent systems introduce a coordination layer on top of these pillars. When one agent delegates to another, you need an orchestration protocol, a shared memory contract, and a way to propagate failures up the chain without silently dropping results. Chapter 3 covers these patterns with concrete examples drawn from real shipping systems.