Solve & Evolve Loops connect everyday problem-solving with knowledge maintenance. They capture useful feedback, identify gaps and assess updates so operational experience can inform future use.
Keep knowledge aligned with daily work
Products, procedures and customer needs change after deployment. Corrections and exception handling can reveal gaps in existing knowledge, but recording an interaction does not make it a reliable lesson. Teams need a way to decide what to incorporate and whether it improves the intended outcome.
The approach
The approach draws on the double-loop process in Knowledge-Centered Service (KCS). The Solve Loop uses and develops knowledge through individual interactions; the Evolve Loop examines patterns and knowledge health across operations. Technica AI adapts these ideas to AI knowledge applications.
- Use and record: Capture relevant outcomes, corrections and decision context from permitted operational records.
- Identify gaps: Group recurring issues and candidate improvements.
- Apply appropriate update controls: Use review, testing or configured automatic updates according to the product and change type.
- Assess impact: Check knowledge freshness and compare meaningful operational metrics over time.
Business value
- Reuse operational experience
Make useful lessons available beyond the original interaction.
- Trace knowledge changes
Track updates and their sources without assuming that the model itself has been retrained.
- Measure improvement
Assess whether updates help the target workflow rather than assuming performance will improve automatically.
Application considerations
- Update rules vary by application. AIKNOW Answer reviews and tests proposed updates; AIKNOW Project automatically incorporates knowledge and lessons. AIKNOW Matching updates profiles automatically, while its ontology is maintained separately.
- Define ownership, baseline metrics and a way to address conflicting or incorrect knowledge. Research on agent reflection illustrates possible approaches, not a guarantee of improvement in a production deployment.
Related products
- AIKNOW Answer
Proposes knowledge updates from interaction records for review and testing.
- AIKNOW Matching
Automatically updates talent, client and project profiles to inform later recommendations.
- AISECI
A knowledge-creation framework with a target architecture connecting capture, sharing and use.
References
- Consortium for Service Innovation. KCS v6 Practices Guide: The Double Loop Process. https://library.serviceinnovation.org/KCS/KCS_v6/KCS_v6_Practices_Guide/030/025 (checked October 10, 2026)
- Shinn, N. et al. (2023). Reflexion: Language Agents with Verbal Reinforcement Learning. NeurIPS 2023. arXiv:2303.11366. https://arxiv.org/abs/2303.11366