You clicked “Approve” again. You didn’t read what it said. You know you didn’t. And the worst part? You’re going to click it again in about forty seconds.
If you’ve spent any real time with Claude Code, Cursor, or any AI coding agent running through a long session, you know exactly what I’m talking about. The first hour is electric. You read every diff. You scrutinize every function. You feel like a pilot with a brilliant copilot. By hour three, you’re a passenger who stopped looking out the window.
Approval fatigue doesn’t mean you trust the AI more. It means you’ve stopped reading.
This is the paradox nobody in the AI coding hype machine wants to talk about. The entire productivity pitch is: let the agent do more, autonomously, without bugging you. But the moment you actually let it run, you’re trapped in a different kind of hell — a purgatory of permission prompts that you rubber-stamp like a bored middle manager signing expense reports.
You’re not pair programming. You’re babysitting a very fast, very confident intern who occasionally deletes the wrong file.
Here’s what happened to me, and probably to you: I’d run a Claude Code session for two hours. Somewhere around the ninety-minute mark, I stopped reading the diffs. I just clicked Approve. Approve. Approve. Approve. Then the build broke. And I had no idea what changed, when it changed, or which approval was the one that detonated everything. I was staring at a terminal like a detective who forgot to take notes at the crime scene.
The problem isn’t that AI agents make mistakes. The problem is that they make mistakes at a speed and volume that makes human oversight theater.
So here’s the uncomfortable truth: real-time monitoring of AI coding agents is a broken model. It doesn’t scale. You either interrupt the flow constantly — which kills the productivity you were chasing — or you let it run and hope for the best, which is not a strategy, it’s a prayer.
This is why I started paying attention to a small macOS menu-bar app called IAXT. It does one thing that sounds almost too simple to matter: it records what your AI coding agents do on your Mac, so you can review the entire session afterward instead of trying to catch problems live.
And here’s where most people get it wrong. They hear “records what agents do” and think: surveillance. Monitoring. Big Brother for your IDE. That’s the surface read, and it’s completely missing the point.
You don’t need to watch the chef cook every meal. But you’d better be able to inspect the kitchen after dinner.
The real value of recording agent sessions isn’t surveillance — it’s shared memory. Think about how logging transformed software engineering. Before structured logging, debugging production systems was archaeology — digging through memory and guessing. After logging, it became forensics. You could replay what happened, find the exact moment things went sideways, and fix the root cause.
AI coding agents need the same thing. Right now, when a session goes wrong, you’re doing archaeology. You’re scrolling through a terminal buffer, trying to reconstruct what happened from fragments and vibes. A recording tool turns that into forensics. You get a replayable, reviewable log of every action the agent took, every file it touched, every command it ran.
But here’s the twist that actually matters: this isn’t just about catching mistakes. It’s about building trust over time.
Trust in AI agents isn’t binary. It’s not “I trust it” or “I don’t.” It’s earned through repeated, verifiable experiences — the same way you build trust with a human collaborator. You watch what they do. You check their work. Over time, you develop a calibrated sense of where they’re reliable and where they’re not. Without a record of what actually happened, you can never build that calibration. You’re stuck in a permanent state of either blind faith or paranoid micromanagement.
The future of AI coding isn’t better agents. It’s better records of what those agents actually did.
Think about the teams using AI agents right now. A developer runs a Cursor session, makes changes, ships them. Another developer on the same team has no idea what the agent did, what it tried, what it rejected. There’s no shared context, no audit trail, no institutional memory. The agent’s work is invisible — which means it’s unreviewable, unimprovable, and ultimately untrustworthy at scale.
This is the bottleneck that’s coming. Not model performance. Not context windows. Not tool integration. Trust and auditability. The teams that figure out how to make AI agent sessions transparent, reviewable, and shareable will move ten times faster than the teams still doing real-time babysitting.
Because here’s the thing about approval fatigue: it’s not a discipline problem. You can’t fix it by “just reading more carefully.” It’s a structural problem. The entire interaction model of interrupting humans for every agent action is fundamentally incompatible with autonomous workflows. You need a different model entirely.
Record now. Review later. Audit when something breaks.
Every great collaboration eventually needs a paper trail. AI agents are no exception.
If you’re using AI coding agents — and at this point, if you’re not, you will be soon — you need to stop pretending that real-time oversight is working. It isn’t. You’re clicking Approve on autopilot, and you know it. The question isn’t whether to let agents run autonomously. That ship has sailed. The question is whether you’ll have the records to understand what they did when you weren’t watching.
Because the scariest part of AI coding isn’t what the agent does wrong. It’s what you approved without reading.
FAQ
Q: Isn't this just surveillance for your IDE? Why would I want my every action recorded?
A: No. Surveillance implies someone is watching you in real-time to catch you doing something wrong. This is the opposite — it's a log you review after the fact, like server logs or git history. You're not surveilling yourself. You're creating an audit trail for an autonomous agent that operates too fast for real-time human oversight to mean anything.
Q: How is this different from just scrolling back through my terminal history?
A: Terminal buffers are ephemeral, incomplete, and lossy. They show output but not context — not which file was modified, not what the agent tried and rejected, not the sequence of decisions. A dedicated recording tool captures the full session as a structured, reviewable artifact. It's the difference between reading a server's stdout and having proper structured logging with traces.
Q: Won't AI agents just get good enough that we won't need monitoring tools?
A: This is the same argument people made about not needing logging because 'code will just be correct.' Agents will get better, but complexity scales faster than reliability. As agents take on larger, more interconnected tasks, the blast radius of a single bad decision grows. The better agents get, the more autonomy we'll give them, and the more critical auditability becomes. Trust scales with transparency, not with capability.