Agentic AI

A chatbot responds to a message and stops. An agent uses the model to plan a sequence of steps, call tools, observe results, and decide what to do next — looping autonomously toward a goal rather than waiting for the next human turn each time.

They help a lot. Prompt engineering is how you instruct the individual reasoning steps inside an agent, and RAG is usually how an agent grounds its decisions in real data. You can start agents without mastering either first, but you'll hit their limits quickly without that foundation.

LangGraph gives you explicit, inspectable control over agent state and branching, which suits long-running or high-stakes workflows. CrewAI is faster to get a multi-agent prototype running with role-based agents. Many teams prototype in one and move to the other — or both — as requirements firm up.

Mostly reliability: agents that work in a demo can fail unpredictably on edge cases, loop indefinitely, or take an unsafe action without a human noticing. Closing that gap is exactly what evaluation, guardrails, and human-in-the-loop checkpoints are for — which is why they're core steps on this roadmap, not optional extras.

Reach for multiple agents when a task cleanly splits into distinct roles or expertise (research vs writing vs review), or when parallelizing sub-tasks meaningfully speeds things up. A single well-designed agent with good tools is often simpler to build, debug, and evaluate — start there before adding orchestration complexity.

Be ready to walk through a real agent you built — how you designed the loop, what tools it had and why, how you handled memory and failure cases, and how you evaluated whether it actually completed tasks correctly. A traced, evaluated agent beats a polished demo video.