Agentic systems
Agentic
LLM-driven loops that plan, take actions in the world (call tools, edit files, hit APIs), observe results, and iterate — rather than just answering a single prompt. The dominant 2026 paradigm for AI engineering.
An "agentic" system is one where the LLM is in a loop: it picks an action, executes it (via tools, code, or another agent), reads the result, and decides what to do next. Compare to a chat completion, where the model produces one response and the loop is closed.
The agentic shift happened because tool-use got reliable enough that you can string 20+ steps without the agent collapsing. Coding agents (Claude Code, Cursor Agent, Aider), research agents (Deep Research, OpenAI's o-series with tools), and operator-style agents (browse-the-web) are all expressions of the same pattern.
Production agentic systems care about: tool reliability, checkpoint/resume, observability (because debugging a 50-step run is hell without traces), cost-per-task budgets, and human-in-the-loop checkpoints for irreversible actions.
See also
MCP
Model Context ProtocolOpen standard from Anthropic (Nov 2024) for connecting AI assistants to external tools, data sources, and prompts. Think "USB for LLM tools" — one protocol, many servers, any client.
Tool use
Tool use / function callingThe LLM emits a structured request to call an external function (search, calculator, API), the host runs it, the result goes back in the next turn. Foundation of every agent worth shipping.
Eval
EvaluationSystematic measurement of LLM/agent quality — accuracy, hallucination rate, latency, cost. The discipline you wish you'd started 6 months earlier. Without it, you're shipping vibes.