# From Intent to Merge: A Hands-On Talk on SDLC in the Agent Era - Joseph Katsioloudes

## Executive summary

This talk introduces 'Entire,' a developer platform designed to rebuild the Software Development Life Cycle (SDLC) for the agentic era. The core problem addressed is the loss of context and decision-making history (the 'missing middle') when using powerful, but forgetful, AI agents. Entire solves this by creating a semantic layer that versions not just the code diff, but the entire session history, allowing developers to track intent, micro-decisions, and evaluations from initial prompt to final merge. Key features include 'Checkpoints' (binding sessions to commits), 'Trails' (a reimagined Pull Request), and 'Runners' and 'Gates' for programmable code review and automated merging.

## Key takeaways

- The Need for Context in Agentic SDLC: Traditional Git only tracks the final artifact (the diff). When using AI agents, the 'missing middle'—the context, micro-decisions, and rabbit holes navigated—is lost. This makes code review incomplete, as the intent behind the code is often unknown. (0:00, 0:28)
- Entire's Semantic Layer Approach: Entire saves conversations and context within the repository, creating a semantic layer. This allows developers to build organizational knowledge and avoid repeating mistakes across different agents and sessions. (0:38)
- Checkpoints and History Preservation: A 'Checkpoint' is a mechanism that binds the entire session (prompt, transcript, tool calls) to a commit, creating a hidden branch (`.checkpoints`). This ensures that the full history, not just the code change, is versioned. (4:30)
- Trails: The Reimagined Pull Request: The platform introduces 'Trails,' which are a reimagination of the PR designed for AI. They maintain the full conversation history and allow for structured review, including tracking confidence levels and findings. (12:00)
- Programmable Review with Runners and Gates: Review is enhanced by 'Runners' (JSON files configured by prompt) and 'Gates.' Runners execute in sandboxes to address specific findings (e.g., 'address slope'), and Gates enforce configurable approval policies, moving toward automated merging. (13:30)

## Technical details

- Platform Architecture: Entire is an open-source, agent-agnostic platform supporting multiple agents (e.g., Cursor, Cloud Code, Codeex, OpenAI, Anthropic). It is designed to operate on top of existing Git hosts (like GitHub) initially, with plans for native repositories. (11:20)
- CLI Commands and Context Retrieval: Developers can explore the repository history and context using CLI commands like `search`, `explain`, and `blame`. These commands leverage the semantic layer to understand intent, which is crucial for debugging and investigating regressions. (8:20)
- Distributed Hosting: To combat downtime issues with traditional Git hosts, Entire offers distributed hosting by creating local regions globally, allowing agents to continue operating even if the primary Git host is unavailable. (10:20)

## Practical implications

- Build engineers can implement a more robust, context-aware version control system that captures the full AI-assisted development lifecycle, moving beyond simple diffs.
- The concept of 'Trails' and 'Runners' provides a framework for integrating automated, programmatic quality gates into the PR process, reducing manual review burden.
- The platform's focus on semantic search and graph representation of codebases can significantly reduce token usage and improve agent efficiency when investigating complex issues.

## Topics

Software Development Life Cycle (SDLC), AI Agents, Version Control Systems (Git), Code Review, Build Automation, Entire

Source: https://www.youtube.com/watch?v=QUwDYXNgx0E