Managed AI, Evaluation & AgentOps
Measure retrieval, model and agent behavior over time so releases, incidents and optimization decisions are based on explicit evidence.
Buyer Intent
For AI Evaluation & Observability, evaluation baselines is usually the first thing that determines scope, before any question about which model or vendor to use.
For AI Evaluation & Observability, the useful starting point is a concrete workflow, decision or system boundary. PlanckCyber defines the outcome, current baseline, dependencies and controls before deciding whether AI, standard software or a combination is the right implementation.
Common Questions
Cost tracks the number of systems being monitored and how often they change, not the evaluation tooling itself. A single model behind a stable prompt is cheap to evaluate; a system with frequent prompt or retrieval changes needs a running evaluation practice, which is a different scope than a one-time check. An AI Systems Review is a practical way to see what a specific system actually needs before committing to an ongoing evaluation program.
A company built for this work treats evaluation as a standing practice — baselines, version tracking, incident response — rather than a single accuracy report delivered at launch. Ask a candidate how they would detect and trace a quality regression after a prompt change ships. A firm without a concrete answer for ongoing monitoring is describing a one-time test, not an evaluation and observability capability.
Decision Questions
Look for a partner who defines evaluation baselines before touching production — what "good" looks like for this specific model, retrieval step or agent, measured against real examples rather than general benchmarks. Ask how they handle prompt and version changes: a system that can silently regress after an update is more dangerous than one that fails loudly, because nobody notices until a customer does.
Compare how each company handles incidents. A partner should explain how they would detect a quality drop, trace it to a specific change, and roll back or patch it — and how quickly that loop runs. Ask about cost and latency budgets too: a system evaluated only on accuracy, with no attention to what each query costs or how long it takes, is incomplete.
An evaluation engagement should establish a baseline dataset and metric for the specific system, instrument version and prompt changes so regressions are traceable, and define an incident process for when quality drops in production. It should also set cost and latency budgets alongside quality targets, since an accurate system that is too slow or expensive is not actually usable.
The biggest risk is treating evaluation as a one-time test instead of an ongoing practice — a system that passed review at launch can drift as models, prompts or underlying data change. A second risk is measuring only accuracy while ignoring cost and latency, which can make a technically correct system unaffordable or too slow for the workflow it supports.
Implementation Factors
Practical Delivery
Evaluation starts with a baseline built from real examples for the specific model, retrieval step or agent in question — not a generic benchmark score. That baseline becomes the reference point every future change is measured against, so a prompt edit or model swap can be judged on evidence instead of impression.
Version and prompt changes are tracked so a quality drop can be traced to a specific change rather than treated as a mystery. Cost and latency are measured alongside accuracy, and an incident process defines what happens when a metric moves the wrong way — detect, trace, roll back or patch, and confirm the fix worked.
Start with the problem
You do not need a specification. Tell us what you are trying to improve.
