写作
英文文章
筛选文章 全部文章
专题、教程与项目实践
第 4 页 · 12 / 70-
Splash: a local LLM inference stack built for each model Splash combines model packages, kernels, memory planning, and compatible local APIs into a model-specific inference stack for coding agents.
-
Turn production agent failures into regression tests Use Kitaru Replay, Evaluator, and Regression Suite to preserve production failures as repeatable agent tests, with tool policies and CI gates.
-
Why more agent tools can mean worse tool selection How Tool Search loads definitions on demand, affects prompt caching, and differs from MCP connections and ARD resource discovery.
-
Claude merges Chat and Cowork to reduce mode choices Claude combines Chat and Cowork in one conversation, routes capabilities automatically, and keeps task state and permissions visible.
-
Foreman: an independent supervisor for coding agents How Foreman separates a coding agent from supervision, using runtime evidence, Jev judgments, and deterministic policy to continue, verify, stop, or escalate.
-
Should an agent keep going or start a fresh attempt? Use When Agents Slow Down, Reflexion, Hyperband, and agent evaluation to compare continued revision with independent attempts under reproducible budgets.
-
Gemini Live: turnComplete no longer means the task is done Gemini Live Extended Thinking separates conversation turns from background task lifecycles. Clients need interaction_status to track actual completion.
-
Why long tasks need more than one growing conversation How OpenAI Agents API and Anthropic Managed Agents use sessions, context windows, compaction, and persistent state for long-running agents.
-
Skills Manager: sync one skill to multiple coding agents Skills Manager distributes a local skill library to Claude Code, Codex, Cursor, and Gemini CLI while managing version and configuration consistency.
-
What Tau solves by splitting an AI agent into three layers Tau separates model adapters, the agent core, and the application so tools and frontends can work together without depending on each other.
-
Connecting an agent loop to a frontend with events Follow tool execution and repeated model calls, separate streamed deltas from final messages, and explore frontend state with a model-free Python demo.
-
AWS agents propose; deterministic code validates How AWS separates agent proposals, rule checks, human approval, and execution, with code enforcing boundaries and idempotency.