Notes on agents that have to keep working.
Ideas and field notes on durable execution, agent design, recovery, and the operating model behind long-running AI work.
The future of AI interfaces is a workbench
Chat is an excellent command channel for AI. The work itself needs a place to live: a shared environment for objects, plans, previews, approvals, and durable execution.
Latest articles
AI Deployment: The Next Race for Enterprise AI
The enterprise AI race will not be won by model access alone. It will be won by the organizations that can turn frontier capability into governed, repeatable, and measurable work.
Portable AI Agents on a USB Drive
MirrorNeuron can package an AI workflow with its models, knowledge, skills, state, and outputs so it can be carried to an edge machine, imported, and run without depending on the cloud.
Workflow Is Becoming the Software: A Survey of Today’s AI Agent Workflow Stack
AI agents are pushing software away from static scripts and toward adaptive, stateful workflows. This survey maps the research roots, the current solution landscape, and the open problems that still separate demos from dependable production systems.
Context Engineering Is Working Memory Design for AI Agents
The LLM is the accelerator, the agent runtime is the operating system, and context is the working memory layer. Reliable agents need memory management, not just longer prompts.
Workflow Is the New User Interface
The most important interface in AI software may no longer be the chat box. For serious work, users need to see state, progress, checkpoints, recovery, cost, and what the system will do next.
Why We Built MirrorNeuron: Making AI Workflows a First-Class Runtime
AI is not missing another demo. It is missing a reliable runtime for long-lived, stateful, recoverable workflows that users can run, inspect, share, benchmark, and trust.
Software Is Becoming Continuous
AI is pushing software away from one-time requests and toward long-lived processes that observe, decide, wait, recover, and keep working. Continuous software needs runtime metrics, not just response quality.
From Prompts to Blueprints
The future of AI software is not hidden in giant prompt files. It is expressed as reusable workflow structure: state, tools, checkpoints, recovery rules, and measurable success criteria.
The Runtime Is the Product
In AI systems, the product experience is increasingly determined by execution quality: completion, recovery, tool correctness, cost per successful workflow, and how rarely humans must repair the system.
Local-First AI Workflows: Adoption Starts Before the Platform Team
Serious AI software should not require a platform team before it becomes useful. Local-first workflows shorten adoption, improve privacy, lower experimentation cost, and create a clean path from one laptop to shared infrastructure.