Abid Ali Awan writes a tutorial showing how to wrap existing Python functions as tools for an LLM agent using the OpenAI Agents SDK. The process involves decorating a function with `@function_tool`, defining an `Agent` with instructions and a tools list, and letting the `Runner` manage the loop where the model decides which tools to call, what arguments to pass, and when to stop. The example uses a simple website-latency checker that becomes an agent capable of comparing response times across multiple URLs and explaining results in natural language.
- The SDK auto-generates the JSON tool schema from the function signature and docstring; no manual schema is needed.
- The same pattern applies to CSV analysis, server monitoring, log analysis, and API automation.
- Cheaper models such as GPT-5.6 Luna make multi-agent tool-calling systems more affordable at scale.
- The SDK auto-generates the JSON tool schema from the function signature and docstring; no manual schema is needed.
- The same pattern applies to CSV analysis, server monitoring, log analysis, and API automation.
- Cheaper models such as GPT-5.6 Luna make multi-agent tool-calling systems more affordable at scale.
OpenProse is a declarative language for standing AI work, where users write Markdown contracts to describe a desired world state and a deterministic reconciler keeps reality matching it. The project applies classical declarative paradigms (SQL, Terraform, Kubernetes, React) to agent-based systems, using "Responsibilities" as the core unit — standing goals with sections for what they maintain, what they require from upstream, and what wakes them. It ships as a skill installable into any Prose-Complete agent host and runs without a separate binary.
- Tagline: "Stop scripting agents. Declare them."
- Forme, the wiring layer, automatically matches subscriptions between contracts so the dependency graph assembles itself with no manual wiring
- The old LLM-based judge loop was retired entirely in the v2 overhaul; a render fires only when a content-addressed fingerprint moves, with no model in the wake/commit decision
- The reference harness "Reactor" was extracted to its own repo and is labelled experimental (alpha)
- Tagline: "Stop scripting agents. Declare them."
- Forme, the wiring layer, automatically matches subscriptions between contracts so the dependency graph assembles itself with no manual wiring
- The old LLM-based judge loop was retired entirely in the v2 overhaul; a render fires only when a content-addressed fingerprint moves, with no model in the wake/commit decision
- The reference harness "Reactor" was extracted to its own repo and is labelled experimental (alpha)
Anirudh Ramanathan writes that while Anthropic suggests code is no longer the primary bottleneck in development, organizations cannot adopt a single, rigid software development life cycle (SDLC) for all changes. Instead, effective management requires a variety of processes tailored to the risk and complexity of each change—ranging from simple documentation fixes to high-stakes schema migrations—utilizing state machines that react to external evidence rather than fixed workflows.
- A spec-driven approach uses written artifacts like intent documents and plans as versioned drivers for development.
- High-velocity code generation necessitates verification mechanisms (like hooks or automated tests) that provide deterministic gates.
- Effective AI governance requires evidence from outside the agent, such as test results from independent systems, to ensure quality at scale.
- A spec-driven approach uses written artifacts like intent documents and plans as versioned drivers for development.
- High-velocity code generation necessitates verification mechanisms (like hooks or automated tests) that provide deterministic gates.
- Effective AI governance requires evidence from outside the agent, such as test results from independent systems, to ensure quality at scale.
Jiahe Geng writes about RSM-full, an online clustered-memory pipeline designed to optimize the quality-to-token trade-off for long-horizon LLM deployments with limited prompt budgets. By utilizing a cosine-gated max-member merge rule and atom-aware grouped context packing, the method achieves significant performance gains in compact-memory regimes compared to existing baselines like Online K-Means and A-MEM. The approach is particularly effective when maintaining an answer quality of 83% for Full-Context level tasks while utilizing only 32% of the total token cost within a 4k budget.
- RSM-full outperforms Streaming-Proto by +2.97 percentage points on the RealMem benchmark.
- The performance gain is primarily driven by the merge rule and grouped packing rather than just flat concatenation or simple clustering.
- The method reaches its optimal utility in the 2k to 5k prompt token range.
- RSM-full outperforms Streaming-Proto by +2.97 percentage points on the RealMem benchmark.
- The performance gain is primarily driven by the merge rule and grouped packing rather than just flat concatenation or simple clustering.
- The method reaches its optimal utility in the 2k to 5k prompt token range.
Paul Sawers writes that Amazon Web Services (AWS) has released an open-source application called Pizza Bot, which provides developers with an email-inspired inbox to manage autonomous AI agents. Designed specifically for tasks that continue after a user has finished their session, the tool moves away from chat interfaces toward an asynchronous model where completed jobs arrive as threads and urgent decisions are surfaced for human triage.
- The project is now a standalone community project rather than an AWS service.
- It supports multiple models including Anthropic, Amazon Bedrock, Google Gemini, OpenAI, OpenRouter, or local models via Ollama.
- Built using LangGraph and DeepAgents to enable stateful agent execution and persistence through checkpoints.
- Available as a desktop app for macOS, Windows, and Linux, with browser and terminal clients also available.
- The project is now a standalone community project rather than an AWS service.
- It supports multiple models including Anthropic, Amazon Bedrock, Google Gemini, OpenAI, OpenRouter, or local models via Ollama.
- Built using LangGraph and DeepAgents to enable stateful agent execution and persistence through checkpoints.
- Available as a desktop app for macOS, Windows, and Linux, with browser and terminal clients also available.
Frederic Lardinois writes that Harness field CTO Martin Reynolds is addressing the surge in pull requests caused by coding agents, which can increase new code volume from 1.5x to as much as 50x. To manage this "review bottleneck," Harness has launched a rebuilt Code Repository and an AI Code Review product designed specifically for high-frequency agent traffic rather than just human teams. The company's approach focuses on using a software delivery knowledge graph to provide reviewers with context quickly, helping them distinguish critical code changes from routine dependency updates.
- Coding agents can increase the volume of pull requests by 10x to 50x compared to traditional developer workflows.
- Harness rebuilt its repository service as an "AI-first" platform that is Kubernetes-based and runs across multiple clouds.
- The new AI Code Review tool integrates with existing GitHub repositories, allowing teams to use it without migrating their entire codebase.
- Coding agents can increase the volume of pull requests by 10x to 50x compared to traditional developer workflows.
- Harness rebuilt its repository service as an "AI-first" platform that is Kubernetes-based and runs across multiple clouds.
- The new AI Code Review tool integrates with existing GitHub repositories, allowing teams to use it without migrating their entire codebase.
Cobus Greyling provides a practical pattern library, starter templates, and CLI tools for loop engineering using AI coding agents. This repository aims to help developers design systems that orchestrate agents to discover work, execute tasks, verify results, and persist state—moving beyond simple prompting toward automated agentic workflows.
- Includes the `@cobusgreyling/loop` unified CLI with commands like `init`, `doctor`, `status`, `audit`, and `cost`.
- Offers various patterns such as Daily Triage, PR Babysitter, CI Sweeper, and Dependency Sweeper.
- Features a tiered rollout strategy: L1 (report) $rightarrow$ L2 (assisted) $rightarrow$ L3 (unattended).
- Includes tools for observability like `loop-cost` to estimate token spend and ROI.
- Includes the `@cobusgreyling/loop` unified CLI with commands like `init`, `doctor`, `status`, `audit`, and `cost`.
- Offers various patterns such as Daily Triage, PR Babysitter, CI Sweeper, and Dependency Sweeper.
- Features a tiered rollout strategy: L1 (report) $rightarrow$ L2 (assisted) $rightarrow$ L3 (unattended).
- Includes tools for observability like `loop-cost` to estimate token spend and ROI.
Pushpak Chhajed writes about the evolution of project rule systems for AI coding agents, explaining why Laravel Boost moved away from complex semantic search layers in favor of a simple markdown-based approach. To prevent instruction files like `CLAUDE.md` from becoming bloated and consuming excessive context, the team implemented a system using `.ai/rules` containing specific Markdown files linked by a generated two-column index. This "progressive disclosure" method allows agents to efficiently locate relevant project conventions without overwhelming their prompt window or requiring complex vector databases for small rule sets.
- The system uses an automatically updated `index.md` file to help agents map current file paths to specific rule files.
- Agents are encouraged to use a combination of index matching and `grep -rin` to find rules that span multiple directories.
- This approach aligns with advice from the Anthropic Claude Code team regarding progressive disclosure in agentic workflows.
- The solution avoids "staleness" risks associated with maintaining separate vector embeddings for small collections of files.
- The system uses an automatically updated `index.md` file to help agents map current file paths to specific rule files.
- Agents are encouraged to use a combination of index matching and `grep -rin` to find rules that span multiple directories.
- This approach aligns with advice from the Anthropic Claude Code team regarding progressive disclosure in agentic workflows.
- The solution avoids "staleness" risks associated with maintaining separate vector embeddings for small collections of files.
Meredith Shubel writes that Vercel published `design.md`, a public prompt file that cut agent-generated design failures by 57% across 200+ agent runs, though none of the six tested pages was ship-ready. The system has three layers: a prompt encoding design judgment, a public stylesheet for mechanical layout rules, and an evaluation loop that converts human feedback into deterministic checks. A Slack-based agent (`design-agent`) consolidates weekly feedback from GitHub and Figma into proposed guidance updates.
- The comparison test used Codex with GPT-5.5: 39 failure instances with `design.md` versus 91 without.
- Vercel's first attempt to port its internal "product design" skill to a public prompt failed because subjective design language was interpreted differently by each model.
- Recurring complaint counts are tracked over time; if a fix doesn't reduce its count, the fix is flagged for refinement.
- The comparison test used Codex with GPT-5.5: 39 failure instances with `design.md` versus 91 without.
- Vercel's first attempt to port its internal "product design" skill to a public prompt failed because subjective design language was interpreted differently by each model.
- Recurring complaint counts are tracked over time; if a fix doesn't reduce its count, the fix is flagged for refinement.
Leela Kumili writes about DoorDash's Flux platform, which moved LLM-based coding agent workloads from developer laptops to cloud infrastructure, automating 130,000 engineering tasks in a single month and supporting over 25,000 automated code reviews per week. The platform was built after local execution hit limits on power, uptime, and security, and is organized around four primitives: Firecracker microVM sandboxes, an in-house MCP gateway, YAML-defined playbooks, and multiple invocation surfaces (Slack, GitHub, cron, CLI, conversational interfaces).
- Sandboxes achieve a 95th percentile SLO under 5 seconds for full setup, including microVM start, repo clone, build tool install, and agent harness configuration.
- Playbooks can mix agent-driven steps with deterministic code where predictable execution or validation is required.
- The Agent Gateway enforces scoped permissions and logs all agent activity for auditing and policy enforcement.
- DoorDash switched Slack integration from private channels to public threads so engineers could observe agent executions and see how other teams delegated work.
- Sandboxes achieve a 95th percentile SLO under 5 seconds for full setup, including microVM start, repo clone, build tool install, and agent harness configuration.
- Playbooks can mix agent-driven steps with deterministic code where predictable execution or validation is required.
- The Agent Gateway enforces scoped permissions and logs all agent activity for auditing and policy enforcement.
- DoorDash switched Slack integration from private channels to public threads so engineers could observe agent executions and see how other teams delegated work.
