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LLM inference in C/C++. Contribute to ggml-org/llama.cpp development by creating an account on GitHub.
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LLM inference in C/C++. Contribute to ggml-org/llama.cpp development by creating an account on GitHub.
New frontier models ship almost every month. Each release can change the balance of quality, cost, and speed available to teams building with AI. Code review brings those trade-offs into focus: how many bugs can a model catch, how much noise does it create, and what does each review cost?
TL;DR Agent-to-agent communication is becoming an important research area for AI companies. As agents grow more capable, their ability to communicate, plan, challenge one another, and work toward a shared objective may produce gains that cannot be achieved by improving each agent independently.
An extra AI check earns its place by verifying behavior the existing workflow missed. I evaluated Jev, TypeSafe’s structured-judgment model, alongside selected automated checks and Qodo code review.
Engineering organizations are building toward an agentic software factory, where agents take on more of the work at every stage of the SDLC, and the systems that govern quality need to keep pace with that shift.
I spend most of my weeks talking with CTOs and engineering leaders, from consumer apps, and e-commerce, via chip makers and automotive to financial institutions, defense industries and global banks.
AI-related production failures stopped being hypothetical some time ago. In our new research, 89% of organizations report having had an AI-related production incident, and only 3.7% of engineering leaders say their existing processes are sufficient to maintain quality and governance as agents take…
Most engineering organizations have a diagram of their architecture. Almost none of them describe the system as it exists today. That was survivable when code moved at human speed. A service got added every few months. Someone updated the wiki page eventually.
Today, Qodo is excited to release our new Agentic Toolbox, a packaged set of tools and skills that brings Qodo’s Context Engine, independent review, and governance capabilities directly into coding agent workflows.
In OpenAI’s Codex, a change can move from plan to implementation to passing tests in a single session. That workflow becomes even more valuable when quality is built into it from the start, with the agent drawing on what your organization already knows and its work reviewed independently.
AI code review has moved fast. Teams no longer expect just linting and style enforcement, they want a layer that catches real production risk before code ships, and most platforms in this category have raced to add one.
Code generation is only one part of software delivery. These skills make the planning, testing, workflow safety, developer experience, and documentation around the code more explicit. AI coding agents are good at responding to concrete implementation requests.
Part 1 of this series described the Context Engine, the knowledge layer that gives review agents a verified picture of the system a change lands in: repository structure, pull request history, cross-repository relationships, and the requirements behind the work. Context answers what is there.
The way you steer a coding agent shapes where it looks, which constraints it treats as important, and how much work it spends rediscovering a repository. Useful agent guidance still has a hard limit: it can influence behavior without proving that the required behavior even occurred.
Qodo recently introduced a set of configurations for tuning code review to your team’s workflow: when reviews run, which standards apply, how much of each finding a developer sees, and which repositories get stricter treatment. In our most recent release we went further.
Ask an engineering team about integrations and the question sounds like a checklist. Does it work with GitHub? Does it read Jira? Is there a VS Code plugin? Fair questions. But they miss what integrations actually do for code review. Every system Qodo connects to is a source of context.
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