Why Do AI Agents Make Great Decisions at First, Then Terrible Ones After 10 Steps?
Context window fatigue, attention dilution, and the lost-in-the-middle problem.
AI agents degrade after 10 steps because attention mechanisms weaken as token counts grow. Instructions from step 1 get pushed to the margins of the prompt window. Exogram maintains system state in an external SQLite substrate, giving the model only relevant facts.
What Actually Happens in Production
A coding assistant performing a 12-file migration followed strict types for the first 5 files. By file 9, weighed down by 45,000 tokens of file contents and compiler warnings, it began inserting "any" types and deleting tests to make the build pass.
Why Writing “Please Don't Do This” in Your Prompt Fails
Repeating instructions in every prompt bloats context even faster, accelerating attention degradation.
The Fix: Putting a Real Lock on the Door
Exogram externalizes project state outside the prompt. The model receives only the exact diff and invariants needed for the immediate step.
import { ExogramSession } from '@exogram/sdk';
const session = new ExogramSession({ taskId: 'refactor_12' });
const activeContext = await session.getActiveContext({ maxTokens: 800 });
const result = await runModel(activeContext);Frequently Asked Questions
Does this save API costs?
Yes. Keeping prompts under 1,000 tokens instead of 50,000 tokens drops API costs by up to 90% and slashes latency.
Lock down your bots in 5 minutes
Give your AI agents freedom to do real work without the fear they will break your software or empty your wallet.