AI Automation Systems
Automation built on large language models: structured prompting, tool use and multi-step agents applied to real, repetitive tasks rather than demos.
In progress: Actively being built right now.
Placeholder artwork. Project photography, screenshots and video are pending — see the asset checklist in the repository.
Idea
Most repetitive work is a sequence of small decisions with a clear rule behind each one. Language models can execute that sequence if the task is decomposed precisely and given the right tools.
Architecture
Each workflow is defined as a chain: input parsing, a model step with a constrained prompt, a validation step, and an action. Failures are caught at validation rather than at the output.
- Structured prompts with explicit output schemas
- Validation before any action is taken
- Tool/API calls for anything the model should not guess
- Logging so every run can be inspected
Status
Ongoing experimental work. Individual workflows function; the goal is a reusable pattern rather than one-off scripts.
Possible future improvements
Status: ConceptNone of the items below have been built. They are recorded here as direction, not capability.
- Retrieval over a private knowledge base
- Evaluation harness to measure workflow reliability
- Local model deployment for privacy-sensitive tasks