Microsoft has open-sourced FastContext-1.0, a lightweight repository-exploration subagent that separates code repository exploration from task solving in LLM coding agents. It uses parallel read-only tool calls to return compact file paths and line ranges, improving end-to-end accuracy and reducing token usage by up to 60.3%, with the 4B-RL model outperforming a 30B-SFT model on SWE-bench Pro.
Microsoft Releases Open Source FastContext for LLM Coding Agents
Data Intelligence Agents Enable Autonomous Data Querying
Data Intelligence Agents (DIA) deploy autonomous coding agents to streamline enterprise data workflows. The Query Generator matches or exceeds top published results on seven SQL benchmarks across four dialects, showing generalization through natural-language instructions and execution-based architecture.
ExecCritic uses role-specific RL to improve coding agents via test-guided repair
ExecCritic introduces a framework that separates test construction from source-code repair to prevent false confidence in coding agents. The system employs a Test agent and a Repair agent, both backed by Qwen-3.5-35B-A3B, trained separately using reinforcement learning.
Microsoft Research releases Flint, a visualization language for AI-driven chart creation
Microsoft Research has introduced Flint, an open-source visualization intermediate language designed to help AI agents generate expressive and visually polished charts from simple, human-editable specifications. The system addresses the trade-off between verbose, fragile low-level parameters and uninspiring default outputs by using semantic data types to automatically derive optimized scales, layouts, and formatting.
GitHub Copilot LLM Gateway enables BYOK for inline completion
GitHub Copilot allows Bring Your Own Key (BYOK) for its Chat window but explicitly blocks those custom models from being used for inline code auto-completion. The official justification from the VS Code team is a supposed lack of capable Fill-in-the-Middle (FIM) models, a claim the author disputes as incorrect blame-shifting.
Metis: Bridging Text and Code Memory for Self-Evolving Agents
Metis introduces a hierarchical dual-representation memory that combines text and code memory to improve self-evolving agents. It organizes experience into execution plans, facts, and pitfalls, crystallizing reusable plans into validated tools only when justified. Evaluated on AppWorld, Metis achieves up to 20.6% higher task accuracy and 22.8% lower execution cost than ReAct, with better overall balance across accuracy, efficiency, and memory cost.