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Cortexa

Bloomberg terminal for agentic memory

Details

External ID
47228173
Source
HN
Company
—
Product
Cortexa
Website domain
cortexa.ink
Launched
March 3, 2026
Cohort
—
Upvotes
10
Upvotes percentile
0.5781057810578106
Tags
—
Fetched at
Sept. 7, 2026, 9:26 p.m.
Updated at
Sept. 7, 2026, 9:26 p.m.

Description

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 archaeology.What Cortexa does:1. Agent decision forensics (end-to-end “why”): trace outputs/actions back to the exact retrievals, memory writes, and tool calls that caused them.2. Memory write governance: intercept and score memory writes (0–1), and optionally block/quarantine ungrounded entries before they poison future runs.3. Memory hygiene + vector store noise control: automatically detect and remove near-duplicate / low-signal entries so retrieval stays high-quality and storage + inference costs don’t creep up.Why this matters: Observability is the missing layer for agentic AI. Without it, autonomy is fragile: small errors silently compound, deployments become risky, and engineering cost goes up because failures aren’t reproducible or attributable.Who this is for: 1. Teams shipping agentic workflows in production 2. Anyone fighting “unknown why” failures, memory pollution, or runaway context costs 3. Engineers who want auditability + faster debugging loopsSite: https://cortexa.ink/Would love feedback from anyone running agents at scale: 1.What’s the most painful agent failure mode you’ve seen in production? 2.What signals would you want in an “agent terminal” (retrieval diffs, memory blame, tool-call traces, alerts, etc.)?

Enrichment

Theme
AI agent frameworks and developer tools
Vertical
Horizontal
Function
Agent / copilot
Audience
B2B
AI stance
AI-native
Project type
Commercial product
Normalized one-liner
terminal for agentic memory
Manually corrected
False

Could you build this?

Yes A dashboard for inspecting, debugging, and analyzing agent memory and context windows is an observability UI built on top of vector databases and tracing logs.

Discussion

4 comments analyzed.

Concerns raised: Memory pollution at scale with multiple agents, Difficulty distinguishing prompt issues from systemic memory problems, Memory contradictions accumulating across sessions

Feature requests: Memory governance tooling for multi-agent systems, Memory contradiction rate monitoring/alerting

Competitors

Other products that read as similar to this one — 315 launches clear the similarity bar, closest 8 shown.

Attention rank: #147 of 316 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 119 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a agent / copilot tool for Agriculture yet.