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We've observed a systemic pattern in production environments: when AI lacks global business context, it tends to produce fixes that are technically correct but architecturally costly. Here's a real-world example.
The final part of our technical deep dive into achieving an 83.4% pass rate on SWE-bench Verified. This post covers Stage 3: once a patch is generated, how do you prove it actually fixed the bug — rather than just making the tests pass?
The second part of our technical deep dive into achieving an 83.4% pass rate on SWE-bench Verified. This post covers Stage 2: once you have runtime facts, how do you make sure the agent changes the right code?
The first part of our technical deep dive into achieving an 83.4% pass rate on SWE-bench Verified using runtime facts. This post covers Stage 1: How do you make sure a bug reproduction is actually correct before you touch any code?
In our latest SWE-bench Verified tests, we validated a new AI debugging paradigm: systematic debugging based on Runtime Facts. By introducing a dynamic tracing mechanism into the Live-SWE-agent architecture to provide the model with runtime context, we achieved a theoretical combined fix rate of 83.4% using the Google Gemini 3 Pro model, marking the highest known performance on the SWE-bench Verified evaluation to date.
In-depth comparison of 5 leading Debug Agent Skill products: Systematic-debugging, Debug (Inbox Zero), Debugging-strategies, Debug-mode, and Syncause Agent Skill.
AI programming is great, but AI debugging often turns into a 'guessing machine.' Discover how Debug Agent Skills bridge the gap between static code and dynamic execution with Runtime Context.
Tired of AI treating symptoms instead of diseases? The Syncause Debug Skill captures execution trajectories (Runtime Facts), enabling AI to resolve code issues based on empirical evidence, zero guesswork required.
While AI tools promise to boost productivity, the data reveals a counterintuitive truth: coding speed has increased, but debug time has surged.
Coding agents are getting better at writing code. But when it comes to debugging real-world bugs, most AI still struggles — not because the models are weak, but because they lack runtime context.
Why coding agents fail after multiple debugging attempts is not a prompt issue or a model flaw, but a context problem. Repeated retries distort attention, weaken invariants, and trap agents in failing loops, making better context more valuable than more attempts.
We repurposed OpenTelemetry (OTel) from a production observability tool into a local, zero-config debugging context specifically for AI coding agents like Cursor and Copilot.
Cursor recently released Debug Mode, attempting to solve the AI debugging problem. In this blog, we'll talk about its mechanism, pros and cons, and future prospects.
A real-world Java debugging case showing why AI debugging without runtime context leads to token waste and trial-and-error, and how Syncause changes that.
We're thrilled to kick off the beta for Syncause AI debugger, our new tool designed to make debugging with AI actually work.
