Mikel Studio · Issue #6
Signals: Make AI Improvement Measurable
From faster scientific software to verifiable production workflows, the useful question is what your system can prove in practice.
The most valuable AI deployments are becoming easier to measure. In one case, an agent improves the software that researchers already depend on. In another, assurance moves beyond documentation into evidence gathered during real operation. Together, they suggest a practical standard: define the outcome, capture the baseline, and make improvement or failure visible to the people responsible for the system.
The Signal
Use AI where the baseline is hard to argue with
Precision steel gauge blocks, a brass dial indicator, and a polished metal cylinder resting on a dark slate surface.
Anthropic reports that Claude optimised more than 30 open-source biomolecular models in under four weeks, producing an average speedup of roughly 4× and adding a low-memory mode.
The practical significance is not only that an AI system wrote code. It worked on an existing technical bottleneck with outcomes that can be benchmarked: execution time, memory use, correctness, and the range of hardware that can run the software.
That makes optimisation a useful starting point for teams exploring AI agents in specialised codebases. Instead of asking whether the model produced an impressive implementation, choose one constrained workload and measure whether the system improves it without introducing regressions.
A compact pilot can:
- establish a reproducible baseline;
- provide representative inputs and correctness checks;
- let the agent propose and test changes;
- review the resulting code as production engineering; and
- compare runtime, memory, infrastructure requirements, and failure cases.
The fresh opportunity is to treat AI as an experiment-expansion tool for engineers. It can explore more implementation options, but the team still decides which changes are correct, maintainable, and worth adopting. In technical organisations, measurable improvement may be a more reliable proof of value than demonstrations of code generation.
What We Learned
Make verification useful during handoff
System cards and model documentation describe what was evaluated at a point in time. Production workflows need something more operational: evidence that lets another person understand what happened and decide what to do next.
Models, prompts, tools, permissions, and dependencies can change after launch. The assurance question is therefore not just whether an agent passed its original tests. It is whether an engineer, auditor, or incident responder can reconstruct a specific run from the deployed system.
For one consequential workflow, capture the evidence needed to answer:
- What did the agent attempt?
- Which information and tools did it use?
- What authority and approvals were present?
- What changed in an external system?
- Where did the run stop, fail, or await input?
- Can it resume without repeating a side effect?
This reframes continuous verification as a handoff problem. Logs, checkpoints, policy decisions, and evaluation results should help someone who was not present for the run take over safely. A model explanation is not enough; the surrounding system must record the relevant facts.
Start with three scenarios: a normal task, an ambiguous or unauthorised request, and an interrupted run caused by a failed dependency. Use the gaps you find to improve the highest-risk authority boundary, add a missing evaluation case, or make one failure safely resumable. That turns assurance from a report into an operating capability.