Agentic AI & AI Agents
Definition
Agentic AI refers to systems where a large language model does not just answer a single prompt, but runs in a loop: it plans, calls external tools (APIs, databases, browsers, code execution), observes the results, and decides the next step until a goal is reached. An AI agent is the combination of a model, a set of tools, memory/state, and an orchestration loop with guardrails.
Why it matters for AI jobs
Agent engineering is the fastest-growing specialty in AI job postings. Roles like "Forward Deployed Engineer, Agentic Platform" (Cohere) or "Software Engineer, Agentic Tooling" (Scale AI) exist specifically to build these systems for customers. Employers want engineers who can design reliable tool interfaces, handle failure and retries, keep agents safe and observable, and integrate them into real business workflows — classic software engineering skills applied to a new runtime.
Key concepts
- Tool use / function calling — exposing typed functions to the model and executing its structured calls.
- Orchestration loop — the plan → act → observe cycle, with stop conditions and budgets.
- Model Context Protocol (MCP) — an open standard for connecting models to tools and data sources.
- Memory & state — short-term context management plus long-term stores (files, vector DBs).
- Multi-agent patterns — planner/worker, reviewer/critic, parallel fan-out with synthesis.
- Guardrails & safety — permissioning, sandboxing, human-in-the-loop approval for risky actions.
- Observability — tracing every tool call and token spend to debug and control cost.
Learning path
- Write a bare-bones agent loop with the Anthropic or OpenAI API: a while-loop that feeds tool results back to the model. Understand it before using any framework.
- Build and publish an MCP server that wraps a real API you use — this is directly marketable experience.
- Add reliability: timeouts, retries, structured output validation, and cost/step budgets.
- Build one end-to-end business workflow agent (e.g. triage → research → draft reply) with human approval gates.
- Study evaluation of agents: task success rate benchmarks and regression suites for prompts and tools.