Agents & Tool Use
Function calling, ReAct loops, MCP, agent memory architectures and evaluation harnesses.
10concepts
51flashcards
85minutes of reading
- 01 Agent Frameworks Compared LangGraph, CrewAI, AutoGen, and the OpenAI Agents SDK solve different problems; the harder question is whether you need a framework at all.
- 02 Model Context Protocol (MCP) The open standard that replaces bespoke per-tool integrations with one protocol, so any compliant client can talk to any compliant server.
- 03 Planning and Task Decomposition in Agents Why an LLM that reasons well step by step still fails to produce a valid multi-step plan, and how decomposition, external planners, and replanning close the gap.
- 04 Tool Use and Function Calling How models invoke external tools to fetch data, run code, and take actions in the world.
- 05 Agent Evaluation Harnesses Single-output accuracy says nothing about an agent that takes thirty steps; evaluating agents means scoring trajectories, environment state, and reliability across runs.
- 06 Agent Memory Architectures An agent whose only memory is its context window is amnesiac between sessions; persistent memory is the architecture that decides what to keep, where, and how to retrieve it.
- 07 Agentic AI and ReAct From single tool calls to multi-step agents that plan, act, observe, and recover from errors.
- 08 Computer-Use Agents: Operating a GUI Through Pixels How agents that click and type on a real desktop differ from tool-calling agents, why GUI grounding is the bottleneck, and what OSWorld measured that API benchmarks cannot.
- 09 Long-Horizon Agent Reliability Why per-step accuracy compounds into task failure, how METR's time-horizon metric reframes agent capability, and which architectural moves actually raise the exponent.
- 10 Sandboxing and Least Privilege for Agents Why agent security has to be enforced outside the model, how capability scoping and human-in-the-loop gates work, and what the CaMeL design proves about the limits of prompting.