The latest from the AI and MCP ecosystem, curated daily.
A busy week in AI. Here's what mattered across 21 stories from Week of March 23–29, 2026.
A comprehensive practical checklist for agent evaluation covering error analysis, dataset construction, grader design, offline and online evals, and production readiness criteria. One of the most complete guides to building a rigorous agent eval pipeline from scratch. Useful for any team that needs to move from "our agent seems to work" to confident production deployment.
Hugging Face published a guide on running OpenClaw agents locally with open-weight models — covering model selection, setup, and the tradeoffs between hosted and self-hosted agent deployments. Relevant for developers wanting to reduce API costs or run agents in air-gapped environments using open-source models.
Cursor published an arXiv technical report on training Composer 2 — covering continued pretraining on Kimi K2.5, large-scale RL in realistic Cursor sessions, and CursorBench results alongside public benchmarks. The report gives the research community a detailed look at how a production AI coding model is actually trained and evaluated.
LangChain shares their internal methodology for building evals for Deep Agents — how they source data, design metrics, and run targeted experiments over time to make agents more accurate and reliable. The key principle: the best evals directly measure a specific agent behaviour you care about, not proxies. Practical guidance backed by LangChain's own production experience.
LangChain introduces Agent Middleware — a pattern for customising agent harnesses by injecting logic between the LLM and its environment without modifying core harness code. Covers the design pattern, use cases (logging, auth, rate limiting, retry logic), and how it enables teams to build application-specific harnesses on top of LangChain's Deep Agents framework.
Google released Gemini 3.1 Flash Live — a low-latency model variant optimised for real-time conversational agent use cases, with sub-500ms response times for voice and interactive chat. Covers the architecture, streaming API, and how to build agents that feel instant rather than waiting on generation. Key for anyone building voice or real-time agent interfaces.
Cursor applies online reinforcement learning to Composer — serving new model checkpoints to production and using real user interactions as reward signals to ship improved checkpoints multiple times per day. A practical account of how they close the loop between production usage and model training at high cadence. Fascinating engineering for anyone building RL-trained coding models.
LangSmith Fleet now supports shareable skills — letting teams equip agents with reusable knowledge packages for specialised tasks that can be shared across the organisation. Brings the Agent Skills pattern into LangChain's production platform, making it easy to compose and distribute agent capabilities at team scale.
OpenAI explains how their Model Spec works as a public framework governing model behaviour — balancing safety, user freedom, and accountability as AI systems become more capable and agentic. The post covers how the spec is written, how it influences training, and how it evolves. Important reading for developers and researchers thinking about AI alignment and model governance.
Perplexity shares how they built and scaled their voice search agent using OpenAI's Realtime API — covering architecture decisions, latency optimisation, and lessons from shipping to millions of users. A detailed case study of production voice AI at real scale, with practical takeaways for teams building speech-to-speech agents.
Anthropic introduced Auto mode for Claude Code, which lets it execute commands and edit files autonomously without prompting for permission at each step — but with sandboxing guardrails that make it safe to do so. The mode intelligently decides when to proceed vs. pause based on risk level, reducing friction for long-running coding tasks. A meaningful step toward truly autonomous software agents.
OpenAI launched a Safety Bug Bounty program specifically targeting AI abuse and safety risks — including agentic vulnerabilities, prompt injection, and data exfiltration in AI systems. Unlike traditional security bounties, this one focuses on AI-specific attack surfaces. Relevant for developers thinking about hardening their own agent systems.
Cursor launched self-hosted cloud agents — letting enterprises run Cursor's agent execution entirely within their own network, keeping code and tool outputs off Cursor's infrastructure. Addresses the primary enterprise blocker of data residency and network isolation for agentic coding. A significant move for Cursor's enterprise push.
OpenAI released prompt-based teen safety policies for developers via the gpt-oss-safeguard model, providing a programmatic way to moderate age-specific risks in AI products. Developers building consumer-facing apps can integrate these policies directly rather than building their own content moderation layer.
ChatGPT launched richer shopping and product discovery powered by the Agentic Commerce Protocol — enabling product comparisons, merchant integrations, and visual browsing directly in chat. A new surface for developers and merchants to integrate into the ChatGPT ecosystem via the commerce protocol.
ServiceNow AI introduced EVA — an evaluation framework specifically designed for voice agents, covering turn-taking, interruption handling, latency perception, and task completion in speech-to-speech interactions. Fills a gap in agent evaluation tooling where most existing evals focus on text. Useful for any team building production voice agents.
Claude Code gained an Auto mode that lets it decide independently how much autonomy to take on a given task — running commands, editing files, and iterating without waiting for approval at each step. This makes Claude Code substantially more useful for longer-running agentic coding tasks where constant interruption slows things down. A meaningful step toward fully autonomous software engineering agents.
A deep technical post on how to architect agent harnesses that stay reliable across long development sessions — covering state management, context window hygiene, error recovery, and how to structure tool calls so agents don't drift. Anthropic shares patterns from building Claude Code and their internal agent infrastructure. Essential for teams building production-grade agentic systems.
Harrison Chase explains the two authorization models in LangSmith Fleet: Assistants (agents act on behalf of the end user using their own credentials) and Claws (agents use a fixed service credential set). The distinction has major implications for security, auditability, and what actions agents can safely take. Foundational conceptual post for anyone designing agent auth.
A deep engineering post on how Cursor built fast regex search indexes so agents can find text across large monorepos in milliseconds rather than 15+ seconds with ripgrep. Covers the index design, query execution, and the tradeoffs specific to agent tool use patterns. Excellent reference for anyone building search tooling for coding agents.
Anthropic launched computer use capabilities for Claude, allowing it to control a desktop environment — clicking, typing, navigating apps, and executing multi-step workflows autonomously. This brings Claude into the same category as OpenAI’s Operator and Google’s Project Mariner for browser and desktop automation. A major capability expansion for agent use cases.
