Framework
The Tribal Knowledge Capture Loop
How the system captures and reuses how senior engineers solve problems.
How it works
- 1The system watches a senior engineer fix a problem and stores how they did it.
- 2When a less experienced person hits the same issue, the system recalls the fix and recommends it.
- 3Users can correct the memory when AI picks up the wrong pattern.
- 4Active coaching of agents becomes the new responsibility for senior engineers.
Hear it on the podcast
Data agents you can trust in production. | Pradnesh Patil from Altimate AIListen to the episodeMore from this episode
The Four Components of an Agent HarnessThe components that turn general-purpose AI into a trustworthy production resource. Altimate Core with harness components scores 75% on the Agent Data Engineering Benchmark; Claude Code without them scores around 45%. The harness is the difference.The Token Efficiency StackPradnesh's thesis: Sonnet with the right harness components beats Opus without them. The era of unlimited subsidized tokens from frontier labs is ending — routing is the emerging best practice.The Four High-Value Data Use CasesThe highest-value applications for data agents in production.
Get the next framework first
One email a week with the frameworks, tools, and founder stories from the podcast. Free.
More frameworks for AI & Automation
The Responsible AI FoundationA reusable principle stack Punit applies before any AI product ships.The REACTOR Prompt FrameworkPunit's six-part prompting structure that turns any LLM into something close to a senior consultant.Channel-Scoped Knowledge BasesHow Dobbin meets each person where they already work.The Virtual Privacy Advisor PatternA blueprint for the AI agent founders should be building right now.The Trust Curve in AI DisclosureWill frames the disclosure question not as a binary but as a function of industry, demographic, and medium.Structure Beats MarkdownWhat LLMs actually reward inside your content.