AI that acts, not just answers
The research asks when a model should only draft an answer, when it may prepare a tool-assisted action, and where approval must remain with a person.
Candidate workflows include invoice review, support triage, document extraction, and cross-tool preparation. Each requires its own task set, failure criteria, permissions, and review boundary.
Questions a credible model workflow must answer
Training evaluation
Define when domain-specific training is justified and what data, baselines, and holdout tests a credible run would require.
Fine-tuning methods
Compare adaptation methods for narrow tasks without assuming a fine-tuned model is better than a prompted baseline.
Distillation studies
Measure compact and reference models under the same task, quality, latency, cost, and review conditions.
Deployment requirements
Document registry, observability, rollback, permission, and approval requirements before any production use.
Evaluation protocols
Specify repeatable measures for safety, task quality, abstention, latency, cost, and domain fit.
Safety review
Define policy, failure, bias, and tool-use checks required before a model can touch consequential data or actions.
Responsible by default
Any model considered for client use should have documented safety, bias, policy, and failure testing before it touches consequential data or actions.
Reviewers need evidence they can decide on: cited sources, proposed actions, tool results, confidence boundaries, and a record of what was approved or rejected.
Where it could apply
Reviewed automation
A candidate workflow for preparing multi-step actions while keeping permissions, checkpoints, and human approval explicit.
Support triage
A candidate use case for classifying requests, drafting responses, and escalating uncertain or consequential cases.
Tool-assisted workflows
A research scope for coordinating tools and APIs while preserving source context, action logs, and stop conditions.
Document review
A candidate workflow for extracting and summarizing information with citations, confidence boundaries, and human verification.
Questions under review
- Evaluate compact models against declared privacy, quality, latency, and hardware constraints.
- Test whether a single-task model outperforms a simpler rules or prompting baseline.
- Define safe tool-use, planning, stop, and approval boundaries for multi-step workflows.
- Measure whether cited retrieval improves task quality without introducing stale or irrelevant context.
- Evaluate text, image, and structured inputs with source-specific failure criteria.
