MLA 028 AI Agents: Loops, Tools, Memory, Protocols, and Evaluation

What an AI agent actually is, why coding agents got good first, how memory really works, what MCP and A2A standardize, which SDKs are alive, how to evaluate on trajectories, and where the products stand after browser agents contracted. Links Try a walking desk - stay healthy & sharp while you learn & code More OCDevel shows - this one has siblings, each on its own subject and produced the same way

First of two episodes on AI agents. This one is the architecture: the loop, tools and verifiable feedback, memory, the protocols (MCP, A2A, computer use), the SDK landscape, evaluation and observability, the product map, and when multiple agents help. The next episode, OpenClaw and the Personal Agent, applies it to one always-on assistant with security as the centerpiece. Coding-agent products and mechanics live in the vibe coding sequence starting at MLA 22. Agent vs workflow vs chat: the loop A chat model returns a message; a workflow is your code calling a model at fixed steps; an agent is a model that owns the control flow, choosing its next action from what it observes. That puts systems on a spectrum (chat, chat plus tools, workflows, agents) rather than in a binary, the framing Anthropic's Building Effective Agents uses. The loop itself is ReAct (Yao et al.): thought, action, observation, repeat, with the reasoning trace letting the model track and update a plan. What changed by 2026 is not the loop but the infrastructure around it, and every part of that infrastructure is an attack on per-step error compounding. Tools, function calling, and verifiable feedback Function calling: you describe tools as schemas, the model emits a structured call, your code executes it and returns the observation. The model never runs anything itself, which is the security model. Writing effective tools for agents gives the practical rules: few high-impact tools, clear namespaces, meaningful identifiers, token-efficient responses, descriptions treated as prompt engineering. Effective context engineering for AI agents adds the overlap test: if a human cannot say which tool applies, neither can the agent. The central principle: coding agents got good first because tests and compilers give verifiable feedback that catches a bad step inside the same loop that made it. Find or manufacture the verifier before writing the prompt. Memory: context, retrieval, files, episodic "Memory" means four things: the context window (the only memory the model has), retrieval from an external store, files on disk, and episodic records of prior sessions. Most agent memory is files. Anthropic's memory tool is a client-side file protocol (view, create, replace, insert, delete) against storage you own. The hard part is context management, and both labs converged on the same three mechanisms: context editing to clear stale tool results, compaction to summarize near the limit, and notes written to files before summarization. OpenAI's Responses API conversation state has the same shape with a compaction threshold and compact endpoint. Third-party layers Mem0, Letta (from MemGPT), and Zep (temporal knowledge graph) now compete with first-party primitives. Multi-session patterns: Effective harnesses for long-running agents. Protocols: MCP, A2A, computer use Model Context Protocol is the agent-to-tool standard, now a Linux Foundation project with individual-maintainer governance. 2026 additions: elicitation (server asks the user mid-operation), an extensions mechanism, and the async Tasks extension for long-running tools. Every major SDK below consumes it; its cost is the context each connected server's tool list occupies. A2A is the agent-to-agent standard, Google-built, Linux Foundation-hosted, at v1.0 with a steering committee spanning AWS, Cisco, Google, IBM, Microsoft, Salesforce, SAP and ServiceNow. Strong governance, weak observed consumption; worth knowing, not yet worth building on for small teams. Computer use is the universal fallback: Anthropic's compute