--- title: "Agentic Development" description: "7 posts about Agentic Development, summarised, each linking to the original." --- # Agentic Development > 7 posts about Agentic Development, summarised, each linking to the original. ## Articles ### [How Meta Built Forum With AI-Native Engineering](https://yomu.fyi/post/how-meta-built-forum-with-ai-native-engineering.md) - Company: [Meta](https://yomu.fyi/company/meta.md) - Author: Shengbo Guo - Published: Oct 15, 2026 - Type: Problem & solution Meta built Forum using AI-native engineering, treating an AI agent as a development participant rather than a faster typing tool. The team shifted from human-in-the-loop to human-on-the-loop, where the agent autonomously plans, implements, builds, tests, and corrects its work while the engineer sets goals and approves outcomes. Success was measured by verified, production-ready results, not code volume, with automated verification signals like build status and unit tests filtering output before review. Meta adapted the codebase for legibility, gave agents tools to inspect live view hierarchy directly instead of descriptions, and created a separate sandbox build to isolate prototype code from production. This approach helped Forum reach core feature parity with Facebook Groups in eight weeks and caught a memory issue during development that roughly halved memory use and page faults. ### [Scale Agentic Coding Workflows with Meta’s Muse Spark Open on NVIDIA GB300 NVL72](https://yomu.fyi/post/scale-agentic-coding-workflows-with-meta-s-muse-spark-open-on-nvidia-g.md) - Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md) - Author: Tanya Lenz - Published: Oct 15, 2026 - Type: Announcement Meta launches Muse Spark Open, an 816B-parameter mixture-of-experts model with 42B active parameters and a 1M token context window, trained on NVIDIA GB300 NVL72 for agentic coding workflows. It plans, acts, observes, and self-corrects across multistep tasks like multifile refactoring, using tool calls and subagent coordination while holding an entire project in context without breaking it into pieces. Its 1.8B perception encoder natively processes interleaved text, images, video frames, and audio in a single pass. On GB300 NVL72 with speculative decoding, it achieves over 50K tokens per second per GPU and over 600 tokens per second per user. Sparse MoE routing activates only 42B parameters per token, and NVFP4 quantization reduces memory footprint for larger KV cache. The model is available via Hugging Face and NVIDIA NIM, with support from SGLang, vLLM, and TensorRT LLM for serving, and fine-tuning through NeMo AutoModel and Megatron Bridge. ### [Meta Muse Spark Open is live on Together AI](https://yomu.fyi/post/meta-muse-spark-open-is-live-on-together-ai.md) - Company: [Together AI](https://yomu.fyi/company/together-ai.md) - Published: Oct 15, 2026 - Type: Announcement Meta Muse Spark Open, an open-weight model for agentic coding, is now available on Together AI as a Day 0 launch partner with a 1-million-token context window on serverless and dedicated infrastructure. The model is trained on real developer work including multi-file refactors, debugging sessions, and long-running tasks, leading to higher first-attempt accuracy and reliable tool calling. It features long-horizon autonomy with planning, observation, and self-correction, computer use for desktop and mobile, multimodal input accepting text, image, audio, and video, and intent commentary that states actions before execution for auditability. Early access customers Factory and Ollama tested the model pre-launch and are offering it to their users from day zero. Serverless pricing is $1.25 per million input tokens, $4.25 per million output tokens, and $0.15 per million cached input tokens. ### [Building Reliable Data Analytics Agents: Lessons from the KDD Cup](https://yomu.fyi/post/building-reliable-data-analytics-agents-lessons-from-the-kdd-cup.md) - Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md) - Author: Jiwei Liu - Published: Oct 8, 2026 - Type: Explainer The NVIDIA KGMON team placed second in the KDD Cup 2026 Data Agents competition by building a reliable agent system around a small, fixed LLM. The system constrained the agent’s action space through a harness that normalized data access by converting CSV and JSON files into SQLite tables, exposed a limited set of custom functions for schema inspection, SQL queries, and answer writing, and seeded the agent with schema context before analysis. Documents and videos were handled through targeted tools like prose\_helper for extracting rules from documents and keyframe extraction with aligned transcripts for video evidence, keeping the main LLM context focused. The team emphasized inspectability by recording execution traces and running repeated attempts, using evaluation and trajectory review to identify failures and refine the harness. The key lesson is that reliability for data analytics agents often comes from the harness design, not the model’s capabilities. ### [Part 6: An operating system for coding agents: the disciplined build](https://yomu.fyi/post/part-6-an-operating-system-for-coding-agents-the-disciplined-build.md) - Company: [Stack Overflow](https://yomu.fyi/company/stack-overflow.md) - Author: Varun Jindal - Published: Oct 7, 2026 - Type: Explainer Coding agent projects decay because agents rediscover files, contradict past decisions, and stop writing tests. The fix is a harness: a constitution of non-negotiable rules, an autonomy boundary that permits all reversible actions but gates irreversible ones, tiered context files per module, durable per-fact memory, a multi-lens review panel, and a post-edit hook that updates context in the same diff. On Terminal-Bench 2.0, changing only the harness lifted an agent from 52.8% to 66.5%, a 13.7-point gain with the same model. For multiple agents, coordination uses shared append-only logs, an ownership map by directory, claim/release protocols, and a decision log enforced during review. The model is about a tenth of long-term software quality; the harness is the rest and is buildable. ### [Building Git infrastructure for agent-scale development](https://yomu.fyi/post/building-git-infrastructure-for-agent-scale-development.md) - Company: [Github](https://yomu.fyi/company/github.md) - Author: Brian Celenza - Published: Oct 6, 2026 - Type: Problem & solution GitHub is rebuilding its Git infrastructure to handle agentic development, where agents push millions of commits daily and total activity doubled to 473.3 billion events per month. The current Spokes architecture couples durability with scale by storing full repository copies on local disks and using a quorum for writes, making writes slower as read replicas are added. Key challenges include commit turnaround latency per agent, write throughput up 4.9x year over year, merges contending on a single reference, and CI fan-out multiplying each push into thousands of reads. The new design separates storage from compute, using Azure Blob Storage for durability and lightweight workers for reads, shrinks the critical path by coordinating only reference updates, and moves maintenance off the serving path. Internal benchmarks show up to 35 times higher write throughput with independently scaling read capacity, while preserving existing workflows, branch protections, and audit controls. ### [A New Agentic Experience: JetBrains Air in IDEs – EAP Now Open](https://yomu.fyi/post/a-new-agentic-experience-jetbrains-air-in-ides-eap-now-open.md) - Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md) - Author: Dominique Rolink - Published: Oct 1, 2026 - Type: Announcement JetBrains launched the Early Access Program for Air, an agentic experience in its IDEs available as a plugin or in 2026.3 EAP builds. Air orchestrates multiple AI agents in parallel, moving beyond the traditional AI chat model that JetBrains found insufficient for concurrent tasks. It acts as a conduit for existing agents like Codex, GitHub Copilot, Junie, and Claude, requiring no JetBrains AI subscription. The agentic-first UX organizes work into sessions with cost visibility, temporary worktrees, and local or cloud runs, with free Junie Lite for everyday tasks. Air ships with no agents installed, detects agents on the machine, and sends data directly to the user’s provider, while AI Assistant remains available as a separate plugin.