Infrastructure to get any data, run agentic workflows, and launch GTM plays.
New tools for building agents
Agents get budgets and boundaries - Sensemaker
Microsoft shipped more concrete agent controls while Uber put coding agents on a token budget. The agent story is becoming IT management, not demos.
truefoundry/trueforge
The open-source agent harness - the runtime layer that turns an LLM into a working agent.
Framer Blog: Building Agents for Framer
A detailed look at what we learned while building design agents, from canvas-native workflows and CMS to quality, collaboration, speed, cost, community, and external agents.
Staff engineer shows AI spec-driven development workflow
Mesh-LLM/mesh-llm
Distributed AI/LLM for the people. Share compute privately or publicly to power your agents and chat.
OpenPoke: Recreating Poke's Architecture
How Poke's orchestrated multi-agent system works, what OpenPoke replicates, and the lessons for builders shipping AI assistants.
CEO-Bench: Can Agents Play the Long Game?
Language model agents are becoming proficient executors at isolated, short-horizon tasks such as software engineering and customer service. Yet real-world challenges require a combination of sophisticated skills that remain largely untested in agents: (1) navigating long horizons amid uncertainty; (2) acquiring information in noisy environments; (3) adapting to a changing world; (4) orchestrating multiple moving parts toward a coherent goal. We introduce CEO-Bench, which evaluates these capabilities together by simulating a representative real-world task: operating a startup for 500 days. An agent manages pricing, marketing, budgeting, and many other aspects of a fictional company through a programmable Python interface, operating in the same environment and facing the same challenges as a human CEO. Success demands analyzing noisy, interconnected business databases, translating signals into sound strategy, and coordinating many decisions with programming. The strongest agents write sophisticated code that simulates customer cohorts to forecast future cash and mines negotiation history to uncover hidden customer preferences. Even so, most state-of-the-art models struggle in this environment. Only Claude Opus 4.8 and GPT-5.5 finish above the $1M starting balance, and neither consistently turns a profit. CEO-Bench takes a first step toward measuring the intelligence required to drive sustained, adaptive progress over time.
How to Train Your Agent: Building Reliable Agents with RL — Kyle Corbitt, OpenPipe
tiles-notebook/packages/wasm-runner/pages/api/conversation.ts at dev · tilesprivacy/tiles-notebook
A notebook interface that makes working with AI agents easier. - tilesprivacy/tiles-notebook
Introducing eve
Introducing eve, the open-source agent framework from Vercel for building, running, and scaling agents in production, with durable execution, sandboxed compute, approvals, channels, tracing, and evals built in.
ChatGPT Plans | Free, Go, Plus, Pro, Business, and Enterprise
Built-in worktrees and cloud environments for multi-agent workflows
GitHub - tilesprivacy/tiles-notebook at dev
A notebook interface that makes working with AI agents easier. - tilesprivacy/tiles-notebook
New capabilities for building agents on the Anthropic API | Claude
Claude now offers code execution, MCP server connections, file storage, and extended prompt caching through the API—giving developers powerful tools to build agents that analyze data, connect to external systems, and maintain context for longer periods of time.
Meet Foundry: An AI Startup that Builds, Evaluates, and Improves AI Agents
