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IBM Introduces Trajectory-Informed Memory for AI Agents =======================================================
IBM Introduces Trajectory-Informed Memory for AI Agents =======================================================  ### God of Prompt
@godofprompt
🚨 BREAKING: IBM just admitted your AI agent forgets everything the moment it finishes a task.
> Every mistake. Repeated.
> Every inefficiency. Repeated.
> Every failure. Repeated.
They built the fix.
> Every AI agent starts each task from zero:
> No memory of what worked.
> No memory of what failed.
> No memory of the faster path it found yesterday.
IBM built a fix called Trajectory-Informed Memory.
It watches the agent's full execution and extracts three types of reusable tips:
> what worked
> what failed and how it recovered
> what succeeded but wasted steps
Those tips get injected into the agent's prompt next time a similar task appears.
The model stays frozen. No retraining.
Only the memory evolves.
> 14.3 pp gain in scenario completion on tasks never seen before
> Complex tasks: 19.1% → 47.6% scenario completion, a 149% relative increase
> Zero retraining required
The 149% on hard tasks is the number.
These are 50+ step workflows across multiple apps. Exactly where agents break in production.Show More
Mar 14, 2026, 10:30 AM View on X
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One Sentence Summary
IBM's new Trajectory-Informed Memory allows AI agents to learn from past executions without retraining, significantly improving performance on complex tasks.
Summary
IBM has developed a solution called Trajectory-Informed Memory to address the 'forgetfulness' of AI agents. By capturing what worked, what failed, and what was inefficient during execution, these insights are injected into future prompts as reusable tips. This method resulted in a 149% relative increase in completion rates for complex, multi-step workflows (from 19.1% to 47.6%) without requiring any model retraining.
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IBM Introduces Trajectory-Informed Memory for AI Agents |... ===============