NEW RESEARCH REPORT: Overcoming the limitations of coding agents in complex software systems
Verify AI agent reasoning with runtime context
Your coding agents write fast and explain themselves with total confidence. But how do you know any of it is right? A plausible root cause, a neat fix, a clean summary: none of it is proof.
Undo gives your AI agents the runtime context to check their own work: a recording of what your program actually did as it ran. Every prediction gets tested against real execution, so your engineers act on evidence, not on the agent’s word.
Can you trust what the AI tells you?
A year ago, engineering teams asked whether AI would take their jobs, then whether it was any good. Today the question in every room is simpler and harder: how do I know what the agent produced is right?
Confident, but not always correct
Reasoning from code alone, agents have to guess what happened at runtime: which branch ran, which function was skipped, what a value held. So they build confident theories around execution that never happened, and engineers lose hours following them.
Checking the agent’s work is slow
Without evidence, every agent answer has to be verified by hand: reproducing the bug, stepping through code, adding logging. The time AI saved is spent again on proving it right, or the team ships on trust and hopes for the best.
Solution
Evidence-backed AI reasoning
Undo captures a deterministic recording of your program’s execution: every line run, every function called, every state change. That recording grounds your agent’s guesses in what the code actually did. Show me the evidence? It’s in the recording.
Grounding agent reasoning in runtime context
01
Hypothesis
02
Record the run
03
MCP
Test against reality
04
Confirm or correct
Step 01
Let the agent form a hypothesis
Describe the bug, or just the symptoms you’re seeing. Your coding agent inspects the source and predicts where the root cause lies, exactly as it does today.
Step 02
Capture the run once
Undo records the failing execution in a single, self-contained recording. No need to reproduce the issue each time the agent comes up with a new theory.
Step 03
Test the prediction against reality
Through Undo’s MCP server, the agent jumps to the relevant point in the execution and asks itself: did the program do what I predicted? Did that function even run? Did that value change when I said it would?
Step 04
Confirm or correct
The evidence either supports the hypothesis or it doesn’t. The agent confirms its answer, or says “I was wrong, that function never ran” and revises its reasoning. The loop repeats until the conclusion matches the recorded execution, and your engineers can check every step of it.
How engineers verify agents in practice
Catching an agent’s wrong theory before engineers act on it
A compiler team working on complex code-flow routing uses Undo to keep its AI agents honest. The engineer describes the bug, the agent proposes a root cause from the code, and an Undo recording is used to test that hypothesis. When the agent predicts behavior in a function that the recording shows never ran, it catches its own mistake and looks elsewhere, instead of sending an engineer down a dead end.
Keeping agents on track at the edge of their knowledge
When a model reaches the limits of its training data, the recording gives it something better than more prompting: the real execution to learn from. The agent finds patterns in the live run, not just the static code, and adjusts its predictions to match.
To use agents effectively, you have to find a way to verify them, and Undo allows you to do this easily.
MTS Software Engineer
Leading semiconductor company
What your team gains
Trust the output
Every agent conclusion is backed by recorded execution your engineers can inspect. Confidence comes from evidence, not from how sure the agent sounds.
Stop chasing phantom bugs
Agents catch their own wrong turns against the recording, so your engineers no longer burn hours proving or disproving an AI’s guess by hand.
Adopt AI without adding risk
Scale autonomous software engineering across complex C, C++ and Java codebases, knowing your agents are held to the same standard of proof as your engineers. Engineers stay accountable for the final call; verifying it just gets much faster.
Customer success
Expert-Led Enablement for Long-Term Impact
Undo provides more than a tool. We partner with our customers to ensure our customers achieve their objectives. Our three-tier Customer Success program is designed to deliver time-to-value within weeks, lasting engineering impact, and expert guidance at every stage.
FAST, CONFIDENT ONBOARDING
Seamless Integration
We integrate Undo and our MCP server into your infrastructure with minimal disruption, ensuring compatibility with CI/CD pipelines, testing, and workflows.
CI Pipeline Enablement
We configure your CI pipelines to automatically capture and store recordings of failed test runs, then trigger agent triage to investigate them.
SKILLS THAT STICK
Hands-On Product Training
Interactive lab-based sessions delivered by Undo engineers, the experts behind the technology, helping your team get the most from our tools from day one.
Advanced Debugging Workshops
Bring your toughest bugs. We’ll work side-by-side to analyze root causes, improve dynamic logging, and teach best practices for prompting agents to verify their own reasoning.
LONG-TERM PARTNERSHIP
Quarterly Business Reviews
Structured reviews with your key stakeholders to track adoption, measure value, and align on what’s next.
Forward-Deployed Engineers
Undo’s forward-deployed engineers embed with your team to solve complex challenges. Hands-on expertise exactly where you need it.
Want to see how your agents’ reasoning holds up against real execution?