Code, Build and Evaluate agents - excellent Model and Skills/MCP/ACP Support
Agent Skills
AI coding agents take the shortest path to done, which usually means skipping the specs, tests, and reviews that make software reliable at scale. Agent Skill...
Building an Advanced Agentic Harness | Data For Science
From a single pilot to an air campaign: planning, parallelism, memory, verification, and observability for production-shaped agents.
New tools for building agents
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.
Agent Plugins
A portable package format for reusable components that extend AI agents.
StrongDM Software Factory
StrongDM's field notes on non-interactive agentic development: specs + scenarios, validation harnesses, feedback loops, and the supporting components.
StrongDM Software Factory
StrongDM's field notes on non-interactive agentic development: specs + scenarios, validation harnesses, feedback loops, and the supporting components.
Strata Demo video
Strata Demo video
Prime Intellect - The Open Stack for Self-Improving Agents
The compute and infrastructure platform for you to train, evaluate, and deploy your own agentic models.
Prime Intellect - The Open Stack for Self-Improving Agents
The compute and infrastructure platform for you to train, evaluate, and deploy your own agentic models.
On evaluating agents – aunhumano
No amount of evals will replace the need to look at the data, once you have a evals good coverage you’ll be able to decrease the time but it’ll be always a must to just look at the agent traces to identify possible issues or things to improve.
How to Train Your Agent: Building Reliable Agents with RL — Kyle Corbitt, OpenPipe
Code execution with MCP: building more efficient AI agents
Learn how code execution with the Model Context Protocol enables agents to handle more tools while using fewer tokens, reducing context overhead by up to 98.7%.
