🏗 An AI that nails a single function often falls apart when you say "build me the whole app." Designing across files, keeping interfaces in sync, and endlessly debugging cross-file mismatches is not a solo act, it is a team sport. So this work has AI agents play an actual software team. First, several Architect agents each sketch a competing design (a Software Design Sketch), and a CTO agent scores them 0 to 8 on structural validity and interface consistency, then picks the best. The chosen design is normalized into a contract that a machine can verify: file ownership, public APIs, dependency edges, and even acyclicity. In the implementation phase, Developer agents write only their assigned files, in dependency order, with just the minimal context they need. Coordination stays lightweight through Git. When each one commits to its branch, it leaves a structured note of the exported symbols it changed and which files are affected, so interface changes propagate without sharing full file contents. Finally, a QA agent runs tests after each dependency layer and routes failures back to the responsible developer. It mirrors how human teams actually ship. This work, CodeTeam: An LLM-Powered Multi-Agent Framework for Repository-Level Code Generation, reaches a 42.3% average test pass rate (SFT setting) on NL2Repo-Bench, validated with real upstream pytest suites, and wins on 15 of 19 repositories against the CodeS baseline. The highlight is that structural quality translates into code that actually runs correctly. URL: #CodeGeneration# #AIAgents#

# Codex Features and Practical Usage 🚀 "One agent for everywhere you code." OpenAI Codex is an AI coding agent you can hand entire tasks to — from generation to understanding, review, and debugging. 🏷️ Title: Codex Fundamentals 🔗 URL: 📘 Overview Codex is OpenAI's AI coding agent for software development. Rather than just autocompleting code, it reads your existing project structure and conventions and carries out tasks autonomously. It is built into the ChatGPT Plus, Pro, Business, Edu, and Enterprise plans. ⚙️ How It Works Codex centers on five core capabilities. ・Code generation: describe what you want, and it writes code that fits your existing structure and naming conventions. ・Codebase understanding: it reads complex or legacy code and explains how the system is organized. ・Code review: it surfaces bugs, logic errors, and unhandled edge cases. ・Debugging: it traces failures, diagnoses root causes, and proposes targeted fixes. ・Task automation: it handles refactors, tests, migrations, and setup workflows. Underpinning all of this are two foundations that keep it safe: a sandbox that defines execution boundaries, and an approval policy that decides when to stop and ask. 🛠️ Practical Usage Codex's hallmark is that it runs "everywhere you code," through several entry points. ・CLI: launch `codex` in your terminal and work interactively ・IDE extension: delegate right from your editor ・Web / cloud: run tasks on repos you do not have locally, in parallel ・GitHub integration: ask for a review with `@/codex review` on a PR ・Slack integration: mention `@/codex` in a thread to kick off a task A good path is to start with the CLI via `npm i -g @/openai/codex`, then expand into GitHub and Slack as you get comfortable. 💡 Use Cases Practical patterns include: on day one in an unfamiliar repo, asking "Tell me about this project" to grasp the big picture; having bugs cleaned up before review; or delegating a tedious bulk refactor wholesale. Humans stay focused on direction and review. ⚠️ Caveats Codex is an autonomous agent that reads/writes files and runs commands. Create Git checkpoints (commits) before and after tasks so you can always roll back safely. Authenticating with a ChatGPT account is recommended; some functionality may be limited with API-key auth. #OpenAICodex# #AICoding#

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Two powerful new models now available on Red Hat AI Inference on IBM Cloud. Two different strengths. ☑️ Granite 4.1 30B for enterprise assistants, RAG, code generation, and agentic workflows ☑️ Gemma 4 31B IT for multimodal reasoning, image understanding, and long-context applications Both are available via familiar OpenAI-compatible APIs. Nice work by the team expanding the model catalog and giving developers more choice without changing how they build and integrate AI applications. Getting started with these models on Red Hat AI Inference on IBM cloud:

Most AI narratives in infrastructure start with technology: models, prompts, copilots, and code generation. 🤖 However, that framing is backwards. 😵‍💫 The right framing is workflow-first: What do teams need to do across the lifecycle, and where can AI safely remove toil without increasing risk? 💡 Learn how to redefine your cloud operations with agentic workflows and guardrails in this white paper:

A useful but little-known Gemini API feature 💻 Want to build a coding agent with Gemini? Here's where to start. Gemini's "Coding agent setup" provides skills and setup guidance for building agents that automate code generation and modification tasks. 📌 Title: Coding agent setup 🔗 URL: 🧩 Overview A coding agent autonomously generates, modifies, tests, and debugs code. Gemini's coding agent guide provides best practices for skill definitions, prompt design, and tool integration to build such agents. Combined with code execution and function calling, you can create agents that actually write and run code. 🛠 How to use it Follow the guide to define agent skills (code generation, file operations, test execution, etc.) and wire them to Gemini's tool features. The core pattern is code execution for running code and function calling for filesystem and Git operations. Including coding conventions and repo structure in prompts improves accuracy. 🏗 Building it into production ・CI/CD pipelines: have a coding agent auto-generate review comments and fix suggestions on every PR. ・Automated bug fixing: pass error logs and stack traces, get auto-generated fix patches with tests. ・Code migration: let agents execute framework or language version migrations step by step. ・Documentation generation: read code and auto-generate API docs or READMEs. 💡 Use cases 🔧 Automated bug fixes and patch generation 📝 Auto-review and fix suggestions for PRs 🔄 Codebase migration 📚 Auto-generating documentation from code ⚠️ Watch out Agent-generated code always needs review and testing. Never auto-merge to production without human approval. Also consider the security boundary (sandboxing) when agents access the filesystem. ✨ Automate coding incrementally. Start with review assistance and routine fixes, then expand the agent's authority as you build trust in its reliability. #Gemini# #LLM#

A useful but little-known Claude API feature 🌊 Waiting for the entire tool call argument to finish before doing anything? You can start earlier. Claude's Fine-Grained Tool Streaming delivers tool call arguments as they're generated, token by token. It enables real-time UI updates and early processing of large arguments. 📌 Title: Fine-Grained Tool Streaming 🔗 URL: 🧩 Overview Standard tool calls wait for the full argument JSON before execution. But when arguments are large (long code blocks, documents, etc.), waiting for completion wastes time. Fine-Grained Tool Streaming sends partial argument data as streaming events (input_json_delta) while the model is still generating. You can render progress in the UI or start preprocessing in parallel. 🛠 How to use it Call the Messages API in streaming mode. Tool call arguments arrive as input_json_delta events incrementally. Display them in your UI as they arrive, or begin preprocessing once key parts are confirmed. The actual tool execution still happens after the full JSON is complete, but users see progress in real time. 🏗 Building it into production ・Coding UIs: show code being generated in real time in the editor. Users watch the code being written instead of staring at a spinner. ・Document generation: when producing long text as a tool argument, render a live preview as content streams in. ・Data transformation pipelines: start validation or schema checks on partial argument data as soon as key fields are confirmed. ・Progress UIs: visualize streaming progress of tool arguments so users can see what the agent is about to do. 💡 Use cases ⌨️ Real-time code generation preview 📝 Incremental document rendering ✅ Early argument validation 📊 Tool call progress visualization ⚠️ Watch out Streaming data is incomplete JSON, so don't try to parse it as valid JSON mid-stream. Use partial data for preview and preprocessing only; execute the actual tool only after the full JSON arrives. The event handling code is somewhat complex, so leverage your SDK's built-in helpers. ✨ Showing nothing during wait times is a big UX miss. Stream tool arguments to build interfaces that feel responsive and alive. #Claude# #LLM#