Incident Report: Token overflow and why the refactoring agent hallucinated Mongoose schemas at 45k tokens. Staring at a Datadog trace from ...
Incident Report: Token overflow and why the refactoring agent hallucinated Mongoose schemas at 45k tokens.
Staring at a Datadog trace from last Tuesday is what finally broke my faith in massive context windows. We had a supposedly state-of-the-art model chewing through a legacy Express and Node monolith. The Jira ticket was simply to decouple the billing module.
Around file 38, the agent crossed 45,000 tokens.
The AI agent context limits kicked in, but not with a hard crash or a polite API error. It just entered the Dumb Zone. The attention mechanism degraded so badly that it completely forgot the strict data governance rules we hardcoded into the initial system prompt. It started hallucinating a db.collection.fetchBillingAndDrop() method that absolutely does not exist in Mongoose. It looped retries on that fake method, parsing its own error messages and retrying again, until it racked up a $140 OpenAI bill in forty-two minutes.
I wrote a messy Python script at 2 AM to forcefully truncate the payload, attempting some basic LLM context window optimization.
Compaction is absolute garbage.
You drop the oldest tokens to save space, and suddenly the agent forgets it's writing a backend service. It starts trying to output client-side code because it lost the system context from the first message. It is unpredictably lossy. You drop one wrong state detail about the authentication middleware to save a few hundred tokens, and the entire build pipeline chokes on undefined variables an hour later.
Dropping compaction for an OS kernel hypervisor
At ATX Soft, we had to build an escape hatch just to survive our enterprise contracts. We couldn't keep babysitting scripts that forgot their own purpose halfway through a run.
We stopped treating the LLM like a monolithic brain. We started treating it like an OS kernel.
I was testing this locally, my Vivobook's fans screaming as the Node orchestrator maxed out my 20GB of RAM. The kernel doesn't write the code anymore. It dispatches bounded worker threads. We ripped out the sequential message passing and moved to a swarm-native AI architecture.
The main loop assigns a thread to a single file. The thread spins up, executes the refactor, tests it, and then dies.
Instead of passing back a massive 10,000-token trace of every failed syntax error and hallucinated database call to the main prompt, we use episodic memory. The thread only returns a highly compressed episode of the tool calls that actually worked. We dump the tactical noise. We only save the successful diffs.
Enforcing episodic memory to salvage long-horizon runs
Long-horizon AI tasks are impossible if you're carrying the baggage of every wrong turn in the context window.
You have to isolate the execution from the orchestration. That's the only way we're seeing anything resembling better software output. AI software engineering isn't about feeding a model an infinite amount of text until it generates perfect architecture. It's about building a restrictive hypervisor that physically prevents the model from thinking too far ahead or remembering its own mistakes.
We merged the episodic memory PR this morning. The billing module finally decoupled without dropping the user schemas or spamming the database with fake queries. I need to check the thread timeout configs and the dead-letter queue before we trigger the next batch run.
Reference & Citation
Source: Daws, R. (2026, March 13). Mastering AI agent context limits for better software output. DeveloperTech News. Retrieved from developer-tech.com
