Tool Behavior
Expected tool sequences, allowed alternatives, forbidden actions, approval gates, and tool-overreach detection.
AIML Solutions builds custom evaluation harnesses for teams that need to know whether an agentic workflow got better, worse, slower, riskier, or more expensive after a model, prompt, retrieval, or tool change.
Expected tool sequences, allowed alternatives, forbidden actions, approval gates, and tool-overreach detection.
Context precision/recall, citation grounding, faithfulness, source freshness, and abstention behavior.
Latency, retry counts, token/cost budgets, failure rates, and stop-condition discipline.
Baseline comparison, trajectory diffs, score deltas, and CI gates that fail when behavior degrades.
For tool-using agents where prompt, model, config, or tool changes can silently degrade behavior.
For domain retrieval systems that need citation-grounded answers, abstention checks, and strategy comparisons.
For workflows that need prompt-injection fixtures, tool authorization checks, and sandbox-boundary tests.
AIML Solutions uses public-safe repos to demonstrate the underlying engineering patterns without exposing client details, private runtime paths, credentials, or paid-task specifics.
Examples are synthetic and public-safe. Current public claims are limited to demonstrator code, linked repos, and service descriptions.
A good first engagement is a narrow workflow: one agent task family, one retrieval corpus, or one MCP/tool-use boundary. The output should be a working scorecard, a clear pass/fail gate, and a handoff note your team can use.
Final scope depends on access model, security constraints, corpus availability, trace format, and whether the work is advisory, implementation, or ongoing support.