Cortexa – Bloomberg terminal for agentic memory
Hi HN — I’m Prateek Rao. My cofounders and I built Cortexa, which we describe as a Bloomberg terminal for agentic memory. A pattern I keep seeing: when agents misbehave, most teams iterate on prompts and then “fix” it by plugging in a memory layer (vector DB + RAG). That helps sometimes — but it doesn’t guarantee correctness. In practice it often introduces a new failure mode: the agent retrieves something dubious, writes it back to memory as if it’s truth, and that mistake becomes sticky. Over time you get memory pollution, circular hallucination loops, and debugging turns into log…
Cortexa – Bloomberg terminal for agentic memory launched on March 3, 2026 with 10 votes, #1,177 of 23,380 launches that month and more than 90% of that year's launches. The site was not reachable when checked in September 2026.
In plain words
Cortexa is a debugging and governance platform for AI agents that addresses memory corruption issues in systems using vector databases and retrieval-augmented generation. It provides end-to-end tracing to identify why agents make specific decisions by examining retrievals, memory writes, and tool calls. The platform also includes memory write governance that scores and can block or quarantine ungrounded entries before they pollute the system. It is designed for teams building and deploying agentic systems who need to prevent hallucination loops and maintain memory integrity.
written from the facts on this page · September 2026
From the sources
Cortexa is an observability + reliability layer for AI agents. Trace every action and output back to the exact context, memory, and tool calls that caused it. Govern memory writes in real time (score/block/quarantine ungrounded data) and keep vector stores clean to cut hallucinations, risk, and debugging time—reducing development cost.
In the maker’s words, at launch
Hi HN — I’m Prateek Rao. My cofounders and I built Cortexa, which we describe as a Bloomberg terminal for agentic memory. A pattern I keep seeing: when agents misbehave, most teams iterate on prompts and then “fix” it by plugging in a memory layer (vector DB + RAG). That helps sometimes — but it doesn’t guarantee…
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