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Your codebase’s full structure, dependencies, configurations, and conventions become resolved context so agents reason from facts, not inferences
Agents stop reconstructing the repository every query and instead work faster with an authoritative understanding of the code, requiring fewer tokens
Knowledge is generated programmatically with Moderne recipes, so teams can customize Prethink output
Prethink is a structured, machine readable representation of how your codebase actually works, built directly from deep insights from your code. It captures relationships, dependencies, conventions, and architecture so agents can reason from resolved context instead of inferring from raw files and prompts.
Prethink builds a shared, system-level understanding of your codebase using deterministic analysis and customizable recipes, then configures your agents to reference that context directly.
An LST is a fully resolved, compiler-accurate model of the repository, capturing both structure and meaning of the code.
Prethink is generated using a set of recipes that run on the LSTs, bootstrapping repository-level knowledge quickly and consistently—structure, intent, constraints, and relationships.
Prethink recipes output inspectable CSV, Markdown, and CALM artifacts to a repo or context registry; agent configurations are updated to point to Prethink.
Agents reference Prethink to understand resolved knowledge about the code without needing to parse it—shifting agent effort away from inferring and toward execution.
Teams control exactly when and how that context is refreshed—as part of the CI pipeline, on a scheduled cadence, or after major dependency updates and refactors.
Agents learn how code is meant to be written and organized in your environment, reducing rework caused by violating conventions or patterns.
Agents see resolved service endpoints, external calls, and integration points, making it easier to reason about impact and downstream effects of changes
Agents understand compatibility, risk, and upgrade impact across direct and transitive dependencies without relying on incomplete scans or guesswork.
Agents can reason about runtime behavior and security posture based on real configuration, not inferred assumptions
CALM-formatted architecture gives agents an explicit model of system structure, boundaries, and relationships.
Agents can see how code is validated and what tests cover, making it easier to propose safe changes and identify gaps with confidence.
When agents can’t see the whole story, they burn time and tokens piecing together context. Moderne Prethink gives them a shared understanding from the start.
Prethink eliminates repeated context reconstruction, cutting token usage and time spent across sessions and workflows
With real structure and relationships in context, agents hallucinate less and produce more reliable output.
Teams get predictable results because agents work from the same trusted context every time.
AI tools promise speed, but context is the bottleneck. Moderne Prethink changes how coding agents understand real-world codebases.
See how Prethink works under the hood and how to tailor context for your repositories and agent workflows.
The compiler-accurate, format-preserving code model that makes safe, automated modernization possible across thousands of repositories.
