From Automation to Agentic Systems
Start with the deterministic systems you already trust. Then add models, context, tools and control loops without pretending the machinery disappeared.
Automation is not a dirty word
Before we talk about models or agents, we need to remember what dependable automation already looks like.
Evidence before explanation
A good incident explanation is built from observable facts, not from the most confident-sounding story.
Rules, decisions and state
A rule chooses an outcome. State tells the system where it is in a longer piece of work.
Events, queues and workflows
Not every task happens in one request. Learn the machinery that lets work continue safely across time and failure.
Reliability is behaviour
A dependable system has a clear answer for success, failure, delay, duplication and uncertainty.
Build the deterministic investigator
Put the pieces together: evidence, rules, state and safe outcomes in the first useful Workbench milestone.
Where the model belongs
An LLM is a useful but uncertain component. The system around it still owns the decision, the tools and the consequences.