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A six-part follow-up to an agent system design series covering service boundaries, generated contracts, typed agent flows, at-most-once execution, prompt-injection defenses, supervisor-routed subgraphs, and human-in-the-loop review. The author outlines what each part teaches and who the series is aimed at, with more installments coming.
DeepSeek showed that an AI agent given 20 tasks in a 300-message group chat delegated none of them until a two-layer Router/Worker design took the keyboard away from the top agent, cutting slowest-task time from ~30 to under 16 minutes while spending 23.2M cached tokens per run.
An Anthropic engineer released a 90-minute course covering Claude Opus 5.5’s hosted agent architecture, building research agents, scheduled agents, and evaluation and CI/CD, among other topics. It is considered one of the courses most worth watching.
The article discusses the architectural design of DeepSeek’s newly open-sourced dsh-superagent, noting that its goal of “much value and little human attention” aligns with that of the loopx project. It compares the similarities and differences between the two in state/session separation, context optimization, and router delegation mechanisms, while also promoting the related loopx RFCs.
We introduce FACTS-MAS, a domain-agnostic multi-agent forecasting framework that decomposes heterogeneous evidence into specialized agent channels, validates and weights each via leakage-safe historical validation, and fuses them adaptively. Evaluated on Zillow housing inventory and FRED unemployment claims across 15 U.S. metros, it outperforms naive baselines and TimesFM, though a factor-adjusted exponential smoothing model matches its fused performance.
Shared a complete 18-part handwritten Agent tutorial, starting from 100 lines of Python and progressively building tool calling, permissions, context management, Skills, chat platform integrations, scheduled tasks, MCP, sub-Agents, planning, and memory. Each lesson also compares how mainstream Agents like Claude Code, Codex, Grok Bot, and DeepSeek Harness approach the same problems. The biggest takeaway: mainstream products mostly diverge on one question — should the security layer be handled by prompts or by code?
A practitioner shares lessons from a year of running a multi-model router (drafter, critic, long-context searcher) on real work: role separation, verification gates, a provenance-checked lessons file, and small hot context survived, while agent personas, model voting, and long autonomous chains failed.
Alibaba Cloud has open-sourced the AI Agent Handbook, an enterprise Agent implementation whitepaper available free in both Chinese and English. It covers architecture, building, running, governance, tuning, and real-world enterprise case studies, and has already gained 1647 stars and 242 forks in just over a month.
本文提出 Tracer，一种面向 LLM 智能体的轨迹感知工具检索前端，用 trie 索引历史工具调用序列并结合经验与上下文先验对下一步调用进行排序。在六个环境、四个模型上，它在 24 组对比中 21 组提升了工具选择准确率，同时每步输入 token 减少约 61%–80%。
本文介绍了 Uber 法律红线代理从失败的 RAG 演进到四代架构，实现 91% 决策准确率，将合同审查时间缩短 20%+，并赢得 2026 ALM Legalweek 年度最具创新力法务部门奖。文章详细分析了从 RAG 到反馈闭环、语气分层、再到最终代理架构的技术演进路径。
An exploration of the authority gap in architectures where one AI agent monitors another, questioning whether AI-to-AI oversight constitutes real governance or merely shifts the decision-making burden, and where binding authority should ultimately reside.
该论文研究了边缘设备上小型 LLM 智能体在长对话与重人设上下文中的"人设-逻辑干扰"问题，并提出解耦架构 AO-DA，将逻辑推理与人设表达分离到两条推理路径，实验证明解耦后的逻辑路径在上下文污染下保持结构不变性。
一篇架构对比文章，分析 Pi Durable 与 OpenCode 的设计差异：Pi Durable 将模型生成、工具调用等序列化为可调度的持久任务，使工作不再绑定于单个进程，而传统会话式 harness（如 OpenCode、Claude Code、Codex）仍依赖进程内的活动 agent 循环。
The author builds a data analyst agent on Vercel eve where Qwen generates SQL and Jev serves as a separate review layer, while deterministic code enforces read-only access, row limits, and human approval — demonstrating how to split generation, judgment, and safety checks across different layers.
A tweet recommending a paper on harness engineering that breaks down Claude Code (Opus 5.5), Codex, Gemini CLI and other coding agents into the formula agent = model + harness, covering how agents are built, recurring patterns, future directions, and a checklist for building your own.
The author discusses the emerging need for a "capability layer" in AI agent architectures that would standardize reusable capabilities (web search, scraping, validation, summarization) across multiple agents, with versioning, per-agent configuration, and rollback — questioning whether MCP, LangChain, Composio, or similar platforms already solve this.
A practitioner describes reworking a production multi-agent system so that agents become replaceable workers inside a durable architecture of authoritative state, task contracts, capability routing, independent verification, and deterministic checks. The piece argues that reasoning autonomy should be separated from execution authority, mature workflows should shed reasoning in favor of rules and tests, and the real metric is verified useful output per unit of human attention.
An argument that current AI agent memory plugins all boil down to flawed RAG-based recall, and that agents would be better served by explicit, document-based knowledge rather than vector-store snippet retrieval.
The author dissects Claude Code's source code to explain how its multi-agent system layers the Agent tool, a task registry with a command queue, disk-based mailbox teammates, and a deterministic JS workflow engine — all funneled through a shared query executor with isolated contexts and message-only communication.
Anthropic engineers report shifting from writing prompts to building self-prompting agent loops, with Claude agents using a self-managed memory/ directory that is verified and reorganized overnight, reportedly cutting first-time errors by 97% and speeding acceptance by 30%.