The challenge
A professional services firm was deploying AI agents to augment expert workflows in domains like auditing and compliance. While early performance was promising, the organization lacked a clear process for identifying when, how, and why agents should adapt after deployment. The risk: agents would drift into irrelevance, inefficiency, or even harm, without clear internal signals to guide change.
Our approach
Reins AI developed a full framework for agentic adaptation, grounded in cognitive architectures and real-world signals of agent usefulness. We defined adaptation not as simple model improvement, but as organizational learning. Our work included:
- A taxonomy of user frictions tied to capabilities
- A method for triaging adaptation needs across behavior, knowledge, and interaction layers
- KPI frameworks (quality, suitability, efficiency) applicable across dev, QA, and production
- Memory design principles based on procedural, semantic, and episodic distinctions
- A roadmap from short-term fixes to long-term collaborative reasoning agents
Outcome
The client will be able to move from passive monitoring to active adaptation, with shared definitions of agent behavior and centralized evaluation logic. Agentic capabilities are moving toward clearer scoping, frictions are being mapped to improvement opportunities, and the organization has a path to align product, QA, and development.
What it shows
Agentic systems don’t improve in a vacuum: they require structured adaptation aligned with real user needs. This work reflects Reins AI’s strength in guiding not just AI evaluation, but the evolution of AI systems across time, teams, and trust.
