Tag: agents
All the articles with the tag "agents".
Agent Architectures: ReAct, Plan-and-Execute and Reflection, Compared
Updated:Three ways to spend a model's tokens on thinking around tool use: interleave reasoning and actions (ReAct), plan everything first and then execute (plan-and-execute), or answer, critique and retry (reflection). How each works, the failure each is prone to, and all three run on the same 20 tool-use questions over a fictional database with a 3B model, measuring accuracy, tokens and time.
Agent Memory: Write, Read, Update, Forget
Updated:An agent's context is its working memory; everything else is storage with a retrieval policy. A taxonomy of agent memory, the four operations and the rule each needs, Generative Agents' recency, importance and relevance score worked, MemGPT's tiers, forgetting, memory as files, and privacy. Then retrieval policies measured on 250 conversations whose facts change, with ablations: how often each shows the model an outdated fact, and what fixing that costs.
Computer-Use Agents: Screens as Observations, the Mouse as a Tool
Updated:A computer-use agent operates software the way a person does: it looks at the screen, decides, and clicks or types. The perceive-think-act loop; screenshots versus accessibility trees, measured on real pages (a screenshot is 1,000 to 4,800 tokens; a raw accessibility tree 4,000 to 170,000, pruned 600 to 20,000); coordinate grounding; the action space; what a task costs as screenshots pile up; OSWorld and how far agents have come; and why safety, not reasoning, decides where these agents can be deployed.
Context Engineering: Compaction, Tool Clearing, and Memory
Updated:An agent's window fills with tool output, not with its own thinking. Three API primitives handle that differently: clearing replaces old tool results with stubs, compaction replaces the transcript with a summary, and memory moves notes outside the window. Each one measured on Claude Sonnet 5.5, with what it costs and what it loses.