The standard advice for AI-assisted code review used to be: chunk your repository into manageable pieces, run the model on each chunk, then try to reconcile the results. It was cumbersome, error-prone, and produced reviews that missed the most important class of bugs — the ones that span multiple files.
That advice is now obsolete. DeepSeek V4's 1 million-token context window changes the fundamental economics and ergonomics of AI code analysis. Here's what that means in practice, and how Agent Workbench puts it to work without any setup.
Chunking — splitting a codebase into overlapping text segments and processing each segment independently — was the only viable approach when models topped out at 128k or 200k tokens. For a production codebase of even modest size, that means dozens of separate model calls, none of which can see the full picture.
The failure mode is specific and painful: cross-file reasoning is broken. The most dangerous bugs — security vulnerabilities, data flow issues, subtle race conditions — almost always span multiple files. A function defined in auth.py is called insecurely in api/routes.py. An interface defined in one module is implemented inconsistently in three others. A deprecated argument is still used in six places scattered across the codebase. A chunked review will miss all of these because chunk 1 never sees chunk 7.
The workaround people use — increasing chunk overlap — makes the problem marginally less bad while multiplying your API costs. You end up paying for the same content multiple times and still getting incomplete analysis.
With a 1M-token context, chunking is simply unnecessary for the vast majority of real-world repositories. A codebase of 100,000 lines of Python fits in roughly 400k tokens. A 50-file TypeScript monorepo fits in under 300k. You send it all in one call, and the model reasons over every file simultaneously.
The qualitative difference between chunked and whole-context analysis is substantial. When an AI model can see your entire codebase in a single context window:
/src against what's in /tests and identify untested pathsNone of this requires any prompt engineering on your part. You don't need to write elaborate instructions about "pay attention to cross-file dependencies." The model already sees everything — you just ask the question.
The most valuable applications of whole-context AI code analysis fall into a few clear categories.
Code review before a major merge. Drop your feature branch as a ZIP. Ask for a security review, an assessment of the diff's impact on existing data flows, and a list of edge cases the tests don't cover. You get a structured report in under a minute, without pulling in a senior engineer for a manual review pass.
Onboarding a new engineer. Upload the entire repo and ask: "Generate an architecture overview, a glossary of domain terms, and a step-by-step guide for adding a new API endpoint." The output is grounded entirely in the actual code — no hallucinated frameworks, no invented conventions.
Legacy codebase archaeology. No docs, no comments, no original authors available. Upload the codebase and ask what it does, how data flows through it, and where the highest-risk sections are. This kind of triage used to take days; it now takes minutes.
Q&A over a live repo. "How does the retry logic work in the background job processor?" "Where is user data deleted when an account is closed?" "Is there any place we're logging PII?" Upload once, ask repeatedly, get answers that cite specific file names and function names.
Contract and document set analysis. The same approach works beyond code. Upload a set of contracts, research papers, or specification documents (as a ZIP of PDFs or text files) and ask cross-document questions. Agent Workbench extracts up to 4 million characters from a ZIP — enough for most real-world document sets.
Agent Workbench handles ZIP extraction, file filtering, streaming output, and credit tracking — drop your repo and start asking questions. No setup required.
Try 2 free runs →Agent Workbench is built specifically for this workflow. Here's how it works:
First, export your repository as a ZIP file. You don't need to do any preparation — Agent Workbench automatically extracts the archive, filters out binary files and dependency directories (like node_modules or .venv), and concatenates the source files into a single prompt-ready text stream. The maximum upload size is 20 MB; up to 4 million characters of extracted text are sent to the model.
Second, select a template or write your own prompt. The built-in templates cover the most common tasks:
Third, run it. The output streams in real time — you see the analysis as it's generated, not after a long wait. For a typical mid-size codebase, the full review appears in 60–90 seconds.
The free tier gives you 2 runs per day to try it out on a real project. Paid plans start at €19 for 100 credits (Starter), €49 for 1,000 credits (Pro), or €99 for unlimited runs per month. Credits are consumed per run, not per token, so the pricing is predictable regardless of codebase size.
It's worth being direct about the scope. Agent Workbench is a single-shot analysis tool — you upload, it analyzes, you get a report. It does not create pull requests, run tests, or push changes to your repository. It does not integrate with GitHub, GitLab, or CI/CD pipelines (API access is on the roadmap for Pro users). It is a research and review tool, not an automated coding agent.
That constraint is intentional. The goal is to give you a high-quality, whole-context analysis of whatever you upload, with no configuration overhead. The output is a structured text report you can act on — not autonomous changes to your codebase you need to review after the fact.
For codebases over roughly 150,000 lines, you may approach the 4M character extraction limit. In that case, the practical approach is to analyze the most critical module or subsystem first — authentication, payment processing, data access — rather than the entire monorepo at once.
Ready to run a whole-context code review on your codebase? Drop a ZIP and get a structured analysis in under two minutes.
Open Agent Workbench — 2 free runs