Agents run on terminal memories. Bottle yours.

Software and systems engineering are inextricably intertwined with terminals. Even more so now, with AI coding harnesses like Claude Code, Codex, and Cursor slinging code while engineers drive countless parallel sessions. With “coding is basically solved,” [1] the action is shifting from hand-holding agents in the IDE to orchestrating them across terminals, traces, PRs, and production context.

Transcripts

What happens after and across coding agent sessions matters more than ever. It’s the tribal knowledge few write down and fewer read. We drop in and out of sessions while debugging OTel traces, inspecting database rows, reading LLM eval reports, and switching PRs to keep up with the agentic software factory. We course-correct agents toward outcomes a one-shot prompt couldn’t reach. Then the terminal closes, and it all evaporates.

Runbooks, Skills, and Evals

What if AI could bottle session history as context fuel for future agents? Visr transcribes your agent and shell sessions, tracks their lineage, and turns them into runbooks, skills, and evals that harden your team’s AI harness stack, so agents stop making the same mistakes [2].

Today’s v0.1.0 is the first of many releases aimed at closing the automation gap: moving teams from generic AI agent harnesses to a continuous context loop shaped by their own norms, conventions, and lessons learned. Think team-sized SWE/Terminal Bench [3] for improving Agent Experience across your team.

Unsolved Problems

We believe following Visr’s opinionated approach will solve the following problems:

• Capture fixes so agents stop making the same mistake twice
• Systematically progress AI coding harness stacks without risking regressions
• Offload instructions from reasoning space into deterministic code
• Inform model and effort/reasoning-level choices
• Speed up agent completion times while lowering cost
• Unify coding agent onboarding for teams
• Make this accessible to all AI coding harness users, not just AI/LLM engineers

Today's Release

Visr v0.1.0 unlocks a real-time shell and agent session transcriber paired with intent-based generation of Markdown runbooks. Visr is beta software, and the web UX is intentionally simple in this release: an opt-in surface for showing the loop, learning from real usage, and giving teams something concrete to try. It’s designed to run headlessly (think tmux), so adoption is seamless inside existing AI coding harness stacks without making the web console the center of gravity. Runbooks are a bridge to Agent Skills and Evals; the larger goal is continuous agent context your team can distribute, test, and improve.

Give it a try, and let us know what you think. Thanks!

Footnotes

• [1] Sequoia VC, “Anthropic's Boris Cherny: Why Coding Is Solved, and What Comes Next”
• [2] Garry Tan, X post “How to really stop your agents from making the same mistakes”
• [3] terminal-bench: benchmarks for ai agents in terminal environments