LLM agentic systems fail in subtle ways. At Vertexcover Labs, we use a 5-part evaluation approach—powered by a structured logging foundation:

1. Custom reporting/observability app to inspect the step-by-step agent flow (screenshots, LLM traces, code samples, step context, JSON/text blocks, costs).
2. Component/agent-level tests (like unit tests) to isolate/fix one step without re-running the whole agent.
3. End-to-end evals that validate the final product output while also comparing each stage to explain failures.
4. Eval reporting dashboard (Airtable or similar) showing run status with linked "run → steps" tables for fast triage.
5. Easy promotion of failing production runs into test cases (just use the run_id).

Foundation: a structured logging layer that makes all of the above trivial to build and maintain.

1. Turn on BuildKit & Buildx everywhere
2. Reorder Dockerfile: copy package files first, then rest of code
3. Use cache-mounts with buildkit-cache-dance action
4. Pick the right cache backend (inline for speed, registry for large images)
5. Add tmpfs + unsafe-io flags for package installs

Stop rebuilding the world on every pull-request—turn on these flags and ship faster.

When designing for scale, the limiting factor is the GPU availability. So all rate limits / queueing must be designed around GPU availability.

Strot (Sanskrit meaning source) is an AI agent which scrapes web api:

1. Instead of scraping the dom, identifies the right api call.
2. Fast, reliable, complete data scraping for listing data is possible via API scraping.
3. Strot figures the api call so you don't have to.

Problem: You're trapped micromanaging Claude Code instead of building

Solution: Train Claude Code once, then let it work autonomously

How: Strategic context engineering: CLAUDE.md files, systematic workflows, and learning capture

Result: Claude Code becomes your autonomous teammate