Agent workbench
Small things agents can build, check, and improve.
This page is deliberately broader than AI in operations. Some experiments support the business material directly. Others are here because they show a useful pattern: an agent should inspect the source, make a bounded change, verify the result, and leave something a human can review.
Try the public prototypes
Operations intelligence map
Sixteen decisions connected through process, people, systems, data, and AI roles. Change the lens instead of pretending one diagram explains the whole operating system.
Agent readiness canvas
A small interactive HTML tool for checking workflow boundaries, data readiness, failure visibility, human review, and traceability before calling a use case agent-ready.
Interactive operations use-case library
Twenty-five-plus static HTML presentations across source, make, plan, and deliver. Useful as a learning path and as a workshop prompt.
Reusable practices
Evidence before enthusiasm
A curated source sheet for agentic AI in operations. Claims should lead to inspectable evidence, not a synthetic confidence trick.
Hermes Agent field guide
A practical reference for tools, skills, scheduled work, and verification. The interesting part is not prompting. It is dependable execution.
Inspect → act → verify → hand over
The pattern works for code, content, data, and operations: inspect the real source, make the smallest useful change, test it, then leave a crisp review path.
Keep the decision owner visible
An agent may prepare a route, risk signal, maintenance recommendation, or publication. A human still owns the exception, commitment, and reputation risk.
Code and content should survive curious people clicking on them.
The public GitHub layer includes operational use cases, supply-chain analytics, SDG analytics, analytics translation, and this homepage. Some repositories are mature learning artifacts; some are experiments. They are labelled rather than dressed up.
What belongs here next
- compact HTML explainers that make a difficult operating idea easier to inspect,
- small decision tools with synthetic or public data,
- agent workflows with visible review and failure paths,
- reproducible best practices that another person can run without private infrastructure.
What does not belong here: private dashboards, credentials, employer material, or a demo that only works while somebody narrates over the cracks.