The initial value from Blitzy isn't code generation, but fixing foundational issues. Its onboarding process creates a knowledge graph that improves documentation and test coverage. This provides immediate value by boosting the performance of all existing developer AI tools, like GitHub Copilot, even before writing new code.

The concept isn't about fitting a massive codebase into one context window. Instead, it's a sophisticated architecture using a deep relational knowledge graph to inject only the most relevant, line-level context for a specific task at the exact moment it's needed.

Fine-tuning creates model-specific optimizations that quickly become obsolete. Blitzy favors developing sophisticated, system-level "memory" that captures enterprise-specific context and preferences. This approach is model-agnostic and more durable as base models improve, unlike fine-tuning which requires constant rework.

Tools like Git were designed for human-paced development. AI agents, which can make thousands of changes in parallel, require a new infrastructure layer—real-time repositories, coordination mechanisms, and shared memory—that traditional systems cannot support.

Simple, function-level evals are a "local optimization." Blitzy evaluates system changes by tasking them with completing large, real-world projects (e.g., modifying Apache Spark) and assessing the percentage of completion. This requires human "taste" to judge the gap between functional correctness and true user intent.

Traditional software testing fails because developers can't anticipate every failure mode. Antithesis inverts this by running applications in a deterministic simulation of a hostile real world. By "throwing the kitchen sink" at software—simulating crashes, bad users, and hackers—it empirically discovers rare, critical bugs that manual test cases would miss.

Enterprises are trapped by decades of undocumented code. Rather than ripping and replacing, agentic AI can analyze and understand these complex systems. This enables redesign from the inside out and modernizes the core of the business, bridging the gap between business and IT.

In traditional software, code is the source of truth. For AI agents, behavior is non-deterministic, driven by the black-box model. As a result, runtime traces—which show the agent's step-by-step context and decisions—become the essential artifact for debugging, testing, and collaboration, more so than the code itself.

The agent development process can be significantly sped up by running multiple tasks concurrently. While one agent is engineering a prompt, other processes can be simultaneously scraping websites for a RAG database and conducting deep research on separate platforms. This parallel workflow is key to building complex systems quickly.