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Agent engineering
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Agent engineering
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Instant agent steering and recoverable tool calls
A close reading of Codex instant_interrupt: ending model output, continuing background tools, and preserving state when users change requirements mid-run.
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Agent 中途改需求:即时转向、抢占与可恢复工具调用
拆解 Codex instant_interrupt:用户在 Agent 运行途中补充要求时,模型输出如何收口,后台工具如何继续,以及状态怎样避免重复或丢失。
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OpenShell enforces agent permissions outside the workload
NVIDIA OpenShell places sandbox, network, credential, and policy controls outside the agent workload, with a Supervisor enforcing permission boundaries.
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OpenShell:把 Agent 权限检查放到运行时外层
NVIDIA OpenShell 把 sandbox、网络、凭证和 policy 控制放在 Agent workload 外,由 Supervisor 执行真实权限边界。
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Keep review evidence separate from compacted agent context
Independent Codex Guardian history shows why generation context and review evidence need separate records, with provenance through resume and rollback.
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主 Agent 压缩上下文后,审查证据为什么要独立保存
从 Codex Guardian 历史独立保存出发,解释生成上下文与审查证据为什么不能共用一份压缩摘要,以及 resume、rollback 应如何保留来源。
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Why old CLAUDE.md rules can hinder a model upgrade
Use prompt audits, instruction layers, and migration evals to retain real requirements, reassess model workarounds, and enforce hard limits in code.
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模型升级后,旧 CLAUDE.md 为什么可能拖后腿
拆解 Prompt Audit、指令分层和迁移 Eval:模型能力变化后,哪些规则应保留,哪些模型补丁要重新验收,哪些限制必须下沉到 Hook 或策略层。
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Agent 已获网络权限,为什么连接途中还要继续检查
从 Codex 网络策略更新出发,解释 HTTP 重定向、长响应与 WebSocket 为什么需要可撤销许可,以及工具权限和网络权限为什么必须分层。
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Why agent network permissions need ongoing checks
Codex network policy changes explain revocable permits for redirects, long responses, and WebSockets, and why tool and network permissions need separate layers.
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GitHub Security Lab makes fuzzing an agent feedback loop
GitHub Security Lab Fuzzing Taskflow uses coverage gaps to guide model decisions while deterministic tools execute, measure, and persist results.
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GitHub Security Lab 把 Fuzzing 做成 Agent 反馈环
拆解 GitHub Security Lab 的 Fuzzing Taskflow,模型根据 coverage 缺口决定下一轮,确定性工具继续负责执行、测量与持久化。
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Debugging agents with traces, spans, and OpenTelemetry
The GitHub Copilot OpenTelemetry update explains agent traces, spans, metrics, events, content capture boundaries, and a telemetry adapter for Lora PI Kit.
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Agent 有日志还查不清?读懂 Trace、Span 与 OpenTelemetry
从 GitHub Copilot 的 OpenTelemetry 更新出发,解释 Agent Trace、Span、Metrics、Events、内容采集边界,以及 Lora PI Kit 如何接入标准遥测。
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GPT-6 prompt caching becomes an agent state layer
GPT-6 adds cache breakpoints, diagnostics, prewarming, and mutable configuration. Long-running agents need stable shared prefixes and cache-hit monitoring.
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GPT-6 Prompt Cache 变成了 Agent 的状态层
GPT-6 增加 cache breakpoint、诊断、预热和可变配置。长跑 Agent 需要主动保持共享前缀稳定,并监控缓存命中率。
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A Minecraft agent separates planning from fast decisions
This Minecraft agent uses Astra for slow planning, JEV for frequent action selection, and Mineflayer for execution, with logs and scripts to verify runs.
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Minecraft Agent 把规划和即时决策拆成两层
这个 Minecraft Agent 让 Astra 负责慢规划、JEV 负责高频动作选择、Mineflayer 负责确定性执行,并用日志和验证脚本保留运行证据。
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When to split a task across agents: check dependencies
How task dependencies shape multi-agent results, with SAIGE experiments, ablations, and OpenAI guidance for deciding when to delegate.
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一个任务什么时候该拆成多个 Agent?先看依赖关系
Rethinking Multi-Agent Collaboration 说明多 Agent 的收益取决于任务依赖结构。本文结合 SAIGE 主实验、消融实验和 OpenAI 多 Agent 工程建议,整理可执行的委派判断。
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Jev gives agents typed decisions instead of generated text
TypeSafe Jev uses Choice, Score, and Noul for constrained agent decisions while code retains control of valid actions and side effects.
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Agent 里不是每个判断都需要生成文本:Jev 把决策交给代码
TypeSafe Jev 用 Choice、Score 和 Noul 处理 Agent 的受约束决策,让模型负责模糊判断,代码继续掌握合法动作和副作用。
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Why you cannot just rerun an agent after a crash
WorkflowAgent, step boundaries, and idempotency keys explain safe recovery after crashes, timeouts, and human pauses, with a proposed Lora PI Kit adaptation.
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Agent 跑到一半进程挂了,为什么不能直接重跑?
用 WorkflowAgent、Step 边界和幂等键解释 Agent 在崩溃、超时和人工暂停后怎样安全恢复,并给出 Lora PI Kit 的迁移思路。
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Where agent credentials belong: host, vault, and broker
Where keys belong when coding agents call GitHub, databases, and cloud APIs, and how vaults and credential brokers define access boundaries.
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Agent 的生产密钥该放哪里?Host Boundary、Vault 与 Broker
解释 Coding Agent 调用 GitHub、数据库和云 API 时,应用密钥、执行器密钥和第三方凭据该放在哪里,以及 Vault 与 Credential Broker 的边界。
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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.
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Agent 工具越接越多,为什么反而更容易选错?读懂 Tool Search、MCP 与运行时发现
解释 Tool Search 如何按需加载工具定义,为什么它会影响 Prompt Cache,以及 MCP、Tool Search 和 ARD 分别解决什么问题。
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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.
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Foreman:给 Coding Agent 加一个独立监工
拆解 Foreman 如何把 Coding Agent 的执行循环与独立监督分开,用运行证据、Jev 判断和确定性 policy 决定继续、验证、停止或交给人工。
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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.
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Agent 线上出过的错,怎么自动变成以后每次改动都要重跑的测试?
结合 Kitaru 的 Replay、Evaluator 和 Regression Suite,说明怎样把线上失败固定成可重放的 Agent 回归测试,并用工具策略与评测结果接入 CI。
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长任务为什么不能只靠一条越来越长的对话
结合 OpenAI Agents API 和 Anthropic Managed Agents,解释 Session、Context Window、Compaction 与持久状态怎样支撑跨上下文窗口的长任务 Agent。
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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.
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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.
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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.
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AWS agents propose; deterministic code validates
How AWS separates agent proposals, rule checks, human approval, and execution, with code enforcing boundaries and idempotency.
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A reliable agent might leave the code alone today
GitHub Agentic Workflows jsweep checks whether a change is worthwhile and treats noop as valid, instead of forcing automation to produce a diff.
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一个靠谱的 Agent,今天可能不改代码
从 GitHub Agentic Workflows 的 jsweep 看 Agent 如何先判断有没有值得修改的目标,把 noop 作为正式结果,而不是为了自动化强行制造改动。
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RTK: filter shell output before it reaches a coding agent
RTK filters test, Git, search, and build noise before command output enters model context, applying context engineering at the tool-output boundary.
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RTK:Coding Agent 不该把整段 Shell 输出塞进上下文
介绍 RTK 如何在命令输出进入模型上下文前过滤测试、Git、搜索和构建噪声,把 Context Engineering 做到工具输出边界。
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OpenCompany: version-controlled agent configuration
OpenCompany uses company.toml, agent teams, and human sign-off to put organizational structure and agent configuration into version-controlled files.
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OpenCompany:把公司写成可版本控制的 Agent 配置
OpenCompany 用 company.toml、Agent Team 和 Human Sign-off 把组织结构与 Agent 配置写成可版本控制文件,并明确保留人工签字点。
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jcode moves coding-agent bottlenecks into the harness
How jcode handles multi-agent resources, state synchronization, and verification in its harness, beyond the capabilities of the model alone.
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jcode:把 Coding Agent 的瓶颈从模型转到 Harness
介绍 jcode 如何把多 Agent 的资源、状态同步和验证放到 Harness 层,说明并行 Coding Agent 的瓶颈不只是模型能力。
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OpenAI agent workdays and shifting builder bottlenecks
OpenAI organization-level data suggests that more coding-agent use shifts bottlenecks to task decomposition, concurrency, harnesses, evaluation, and selection.
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OpenAI 研究组织跑到 3.1 Agent 工作日后,Builder 的瓶颈开始转移
OpenAI 的组织级数据说明 Coding Agent 使用规模上升后,瓶颈会转向任务拆分、并发调度、Harness、Eval 和结果选择。
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Agent demos are easy. The harness is the hard part
Strands harness-sdk, Harness MCP Server, and HydraFusion show why agent engineering needs control over context, permissions, state, validation, and recovery.
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Agent Demo 不难,难的是 Harness
从 Strands harness-sdk、Harness MCP Server 和 HydraFusion 看 Agent 工程为什么从模型调用转向上下文、权限、状态、验证与恢复控制。
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AI Engineering Harness: verifiable delivery for AI code
A multi-agent engineering workflow uses isolated worktrees, CI, cold-start review, evidence gates, and layered context to control delivery of AI-generated code.
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AI Engineering Harness:把 AI 代码放进可验证的工程闭环
介绍一个多 Agent 工程工作流,如何用独立 worktree、CI、冷启动审查、证据门和分层上下文控制 AI 生成代码的交付。
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Vercel Eve Agent Framework
A source-backed review of Vercel Eve’s filesystem-first agent model, durable execution, tools, sandboxes, approvals, deployment, and Vercel platform tradeoffs.
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Loop Engineering Harness: controlling agent iterations
Break down planning, execution, evaluation, state management, and stopping rules to keep multi-step agent tasks controlled and traceable.
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Loop Engineering Harness:怎样控制 Agent 的迭代循环
把 Harness 作为一种工程模式,拆解计划、执行、评估、状态管理和终止判断,说明 Agent 多轮任务怎样保持可控和可追溯。
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Building WishLive: A Multi-Agent Runtime
A 48-hour hackathon build of an event-driven multi-agent runtime with registry, workflow, execution, negotiation, settlement, and observability layers.
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