WTF Are Agents? (www.ashpreetbedi.com)

The piece demystifies "agents" as a distinct class of AI programs where an LLM—not hardcoded control flow—decides execution by using supplied context and callable tools. It outlines the canonical agent loop: build context (system/user messages, history, memory), send it to the model, receive a reply or tool call, execute the tool and feed results back, repeat until a final response is produced. A minimal Agno example shows an agent built with Claude, a Sqlite conversation DB, MCP tool integrations, and a FastAPI-based AgentOS runtime, illustrating how tools + model reasoning enable flexible behaviors (e.g., perform N operations without explicit code paths). The article stresses that the real work is productionizing agents: orchestrating runtimes, isolated long-running state, persistent memory, tool integrations, security/guardrails, and evaluation (latency, cost, reliability). Agno positions itself as a turnkey stack—framework for building agents and multi-agent workflows, a FastAPI runtime (AgentOS) for deployment/concurrency/error recovery, and a control plane for testing, monitoring and debugging—aimed at stitching those disciplines together. The takeaway: agents are not magical—they’re LLM-driven software systems whose practical value depends on engineering for state management, safety, and operational scale.