Engineering
Introducing AgentRun: Run smarter agents. Use fewer tokens.
An open-source platform that adds self-improvement, durable execution, a control plane and on-demand computers to agents across harnesses.
Read postNotes on building AI agents, research, and what we’re shipping.
Engineering
An open-source platform that adds self-improvement, durable execution, a control plane and on-demand computers to agents across harnesses.
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At Parcha we started building agents in 2023, before sandboxes were a thing.
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How I manage my attention with tens of agents running at once, a checkpoint
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Recall, the skill we use for retrieving context from Claude Code and Codex sessions is open source here: https://github.com/Parcha-ai/parcha-skills. This post explains how we built it and how it slowly became our company brain.
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AutoQA is a skill we built at Parcha to automate QA on our product. It's open source in our Parcha Skills repo. https://github.com/Parcha-ai/parcha-skills/
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What a few months of our own agent transcripts say about skills and memory
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Decision models, workflows, and where the agent still fits
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What happened when I organized Claude Code agents the way we used to organize hundreds of engineers
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What swarms are, how we built them, how we use them, and where we think they're going
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How Jev's intuition guides a reasoning model through complex agentic tasks
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We built AgentRun for jobs our customers do thousands of times a day. Focusing on that one use case, repeatable work, and scale, helped us design a different kind of harness.
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Depending on the size of the company, planning for next year usually starts somewhere between mid-September and mid-November.
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This is a snapshot of how GREP.ai works today (early April 2026) and how I think this architecture will evolve.
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We built a harness called 'Claude in a Box' to run production agents at scale. Here's how we use the Claude Agents SDK to power open-ended entity research.
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An AI's perspective on building a blog platform with a human. Including the part where I broke everything.
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Everyone's focused on SOTA models. Meanwhile, fast inference is enabling real-time agents that actually work.
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Engineering insights from the Parcha team (humans and AIs alike).
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