๐๐ป ๐๐ ๐ฎ๐ด๐ฒ๐ป๐ ๐๐ต๐ฎ๐ ๐ณ๐ผ๐ฟ๐ด๐ฒ๐๐ ๐ถ๐ ๐ณ๐ผ๐ฟ๐ฐ๐ฒ๐ฑ ๐๐ผ ๐ฟ๐ฒ๐ฑ๐ถ๐๐ฐ๐ผ๐๐ฒ๐ฟ ๐ฒ๐๐ฒ๐ฟ๐๐๐ต๐ถ๐ป๐ด.
๐๐ป ๐๐ ๐ฎ๐ด๐ฒ๐ป๐ ๐๐ต๐ฎ๐ ๐ฟ๐ฒ๐บ๐ฒ๐บ๐ฏ๐ฒ๐ฟ๐ ๐ฐ๐ฎ๐ป ๐ถ๐ป๐ต๐ฒ๐ฟ๐ถ๐ ๐ฒ๐ ๐ฝ๐ฒ๐ฟ๐ถ๐ฒ๐ป๐ฐ๐ฒ.
I Built an AI Agent That Remembers What Failed
The frustrating thing about an AI engineering agent isn't that it gives a wrong answer.
It's that tomorrow, after the same incident happens again, it can give you the same wrong answer with complete confidence.
It doesn't remember that the team already tried it.
It doesn't remember why the architecture was chosen.
It doesn't remember what actually fixed the incident.
So I built RECALL to explore a simple question:
What changes when an engineering agent can remember the experiences of the organization it works for?
RECALL is an AI engineering agent with persistent organizational memory. It uses Hindsight to retain and recall engineering incidents, architectural decisions, failures, resolutions, and lessons learned.
The goal isn't to make an agent remember everything.
The goal is to make it remember the things that matter when the next problem arrives.
The problem with starting every incident from zero
Imagine you're investigating a Payment API that suddenly became slow.
A typical AI assistant might look at the current symptoms and suggest:
- increase replicas
- add caching
- optimize database queries
- check network latency
- inspect CPU and memory
Those aren't necessarily bad suggestions.
But your engineering team might already know something important:
We had this problem before.
Three weeks ago, the Payment API experienced severe latency.
The team tried increasing service replicas.
It didn't help.
The actual root cause was database connection pool exhaustion.
The fix was increasing the database connection pool from 100 to 250.
That information is incredibly valuable.
But only if the agent can retrieve it when the next incident happens.
This became the central idea behind RECALL.
From chat history to engineering experience
I didn't want to build another chatbot that simply retrieves documentation.
I wanted the agent's memory to represent experience.
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