AI Intelligence Layer
AI that reads execution instead of guessing from code.
“Most AI tools try to understand code. AlgoLens first understands execution, then asks AI to explain that truth.”
Grounding AI in traces
AI can explain, summarize, critique, quiz, compare, and teach, but it should not be responsible for deciding what happened during execution. AlgoLens grounds AI on Universal Traces, semantic events, observed complexity, snapshots, errors, invariants, and user annotations. This lowers hallucination risk because the model comments on a structured record instead of inventing runtime behavior from source text alone.
The AI layer can power step explanations, natural-language questions about state, bug hypotheses tied to specific trace events, complexity narratives, refactoring suggestions, learning paths, quiz generation, and comparative reviews across two executions. Because every answer can cite trace steps and semantic events, explanations become more auditable than ordinary code-chat responses.
The architecture should support multiple providers, model routing, prompt templates, safety policies, cacheable explanation artifacts, organization-level configuration, and human-authored educational content that can be mixed with generated explanations. AI becomes a multiplier for trace understanding, not a replacement for the deterministic engines.
Execution ↓ Universal Trace ↓ Semantic Events ↓ AI Context Builder ↓ Explanation · Review · Quiz · Optimization Suggestions