What does it take to keep an agent working after its process dies? Pi creator Mario Zechner and Earendil co-founder Armin Ronacher join KG to explain Pi Durable: explicit tasks, crash recovery, and the primitives for multiplayer agents. We also dig into why Pi added MCP and codemode, and why a harness that can rewrite itself still needs a clear goal.
Full shownotes at fragmentedpodcast.com.
Show NotesPi 1.0, Earendil and Pi Durable- Mario Zechner - Pi’s creator and our guest.
- Armin Ronacher - Earendil co-founder and our guest.
- Pi 1.0 - The stable release and the design choices behind it.
- Pi Durable - Mario’s introduction to the experimental harness for long-running agents.
- “You Said No MCP!” - Why codemode brought MCP and non-LLM models into Pi.
- Blender MCP - The Blender integration Armin uses as an MCP example.
- Pi Durable documentation - Tasks, explicit phases, replay-safe tools, ownership, and storage.
- Temporal - The workflow engine compared with Pi Durable’s explicit task model.
- Absurd Workflows: Durable Execution With Just Postgres - Armin’s earlier workflow design, and the trade-offs discussed here.
- Pi Durable: abort and subagents - How task ownership connects parent work and child conversations.
- Chord - Networked objects, versioned contracts, and hot reload across clients.
- Episode 311 - Self learning harnesses are here - The previous discussion that prompted KG’s questions.
- Prime Agent - The harness discussed while questioning goals, evaluation, and unconstrained rewrites.
- Terminal-Bench - The benchmark used to explain narrow optimization and benchmark gaming.
- Earendil - The team’s broader goal: AI tools that work beyond software engineering.
- Bryan Cantrill’s The Observation Deck - The writing Armin recommends to software engineers.
- Reflecting on Life - Armin’s broader advice on work, commitment, and personal growth.
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