Tequila Framework

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This framework and ecosystem facilitate the development and evolution of JavaScript products driven by intelligent agents, ensuring long-term maintainability, control, and reviewability of automated modifications. Key features include durable context management, structured product architecture, explicit specifications, and governance tools that preserve product meaning, architectural boundaries, and decision transparency across multiple agent interactions. Designed for product owners, technical founders, and engineers using large language model (LLM) agents on real-world JavaScript projects, it addresses challenges related to controlling repeated automated changes, maintaining architectural integrity, and ensuring that product knowledge remains accessible and understandable for humans and future agents alike.

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Podrobnosti

Glavne značilnosti Tequila Framework

• TeqFW structure
  TeqFW provides the structure and rules for the JavaScript product environment.
• Proof of concept
  This website itself was created by an AI agent following human instructions, showing proof of the system.
• ADSM keeps context
  ADSM makes sure all product information and decisions stay stored outside individual chats.
• Control layer tools
  ADSM, TeqFW, and GitHub Flows help keep agent work connected to the real product and its boundaries.
• Need for continuity
  Agent work should stay understandable to humans and useful for future changes.
• Problems with control
  Repeated agent sessions can scatter decisions, making it hard to see what the product is or should be.
• Importance of stability
  The product should have memory, clear specifications, and review trails to keep changes aligned with the original intent.
• Real product importance
  The JavaScript code holds essential business information and design limits for your product.
• Supports existing users
  If you already use agents, you can continue applying them to your product work.
• Designed for product teams
  Made for product owners, developers, and engineers using language models on real JavaScript products.
• Focus on product stability
  Ensure the product's meaning, limits, and review trail last through repeated agent work.
• Agent work inside boundaries
  Agents work within controlled and inspectable parts of the product to ensure safe changes.
• Agent-driven JavaScript products
  Build JavaScript products that agents can safely change and improve over time.

Značke

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