SolutionsAI Engineering
AI Engineering. Calibrate agents against evals.
Define evals for a use case, run the harness (agent, skills, tools, process, memory), measure, adjust, repeat until scores converge. This solution makes that loop operable.
Runs as a service today.
01Capabilities
The calibration loop, made operable.
The other four solutions arrive calibrated. AI Engineering hands you the loop itself: define evals, run, measure, adjust, repeat, on your own agents.
- Eval definition and runs
- Define evals for your use case and run them as real enforced runs, with gates and a journal.
- Harness calibration
- Calibrate a harness against obedience and quality dimensions. Adjust the process, the skills, and the memory; re-run; watch the scores move.
- Process-trace inspection
- Open any run and read its trace: which step ran, which gate held, where it iterated, and why it stopped.
- Quality-convergence tuning
- Tune against the 90-Score Pattern: the runtime's multi-gate weighted scoring.
- Cross-harness comparison
- Run the same process across the 12 supported harnesses through Adapters, and compare their traces and scores side by side.
02Convergence
Watch the scores settle.
Each iteration is a batch of eval runs. As the process and skills are adjusted, the scores move toward the target and their spread tightens. Calibration is done when the runs sit on the line, and stay there when the evals re-run.
The tuning that used to live in one engineer’s head becomes a record the next engineer can re-run. When that engineer moves on, the calibration stays.
03The delta
What a calibration pass changes.
The console reads calibration as movement across the seven measured obedience dimensions, where each sits against the target before and after the process, skills, and memory are tuned.
- Completeness
- Ordering
- Conditionality
- Parallelism
- Granularity
- Aggregation
- Error handling
Before a pass, dimensions sit under their target. A pass tunes each one until it holds on the target, and stays there when the evals re-run. The scores are yours, read from your evals against a target you set.
04Method
What gets measured.
Grounded in the open eval instruments: obedience-benchmark for process-following, and Babysitter’s quality convergence for output. The loop holds with closed and open models alike; a calibrated harness is what lifts an open model to domain work, so the model choice stops carrying the risk.
- Obedience across seven dimensions
- Process-following is scored on completeness, ordering, conditionality, parallelism, granularity, aggregation, and error handling. Obedience and capability are measured apart.
- Quality through weighted gates
- Output quality is scored by the runtime's multi-gate weighted system, iterated until a run clears the target.
- The loop itself, as a product
- This is the calibration methodology applied to your own agents. No benchmark numbers are claimed, and the loop is not automated end to end; a human still reads the traces and sets the target.
05The console
What the console shows.
- An eval-run list with per-run scores.
- Harness calibration curves across iterations, converging toward a target.
- A process-trace view for any run.
- Gate outcomes, pass or fail, on each run.
The loop runs on the same open components it calibrates against, so nothing in the method is hidden. Here the calibration is the whole product, operated with your engineers.
The difference between a tuned harness and a lucky one is a record you can re-run.
Bring your evals; watch the scores converge across iterations.
An AI Engineering demo runs evals you define and shows the scores move across iterations.
Or emailhello@a5c.ai
The intro call is 30 minutes and free.