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AgenticLibContext-Aware Product Management for AI Agent Builders

Context-Aware Product Management for AI Agent Builders

Turn customer requests, agent behaviour analytics and competitor signals by domain and use case into a dynamic product roadmap.

Built for product managers, product owners and product creators within AI Agent companies.

Trusted by teams shipping AI agents
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Why AgenticLib

An entirely context-aware product harness for agentic workflows by vertical.

AgenticLib combines customers requests, lost deal notes, support tickets, agent behaviour, user analytics as well as competitor signals by use case to advise on product feature opportunities ranked by impact and use case cluster expansions.

Built to streamline the process involved in creating an agentic product.

AgenticLib
AgenticLib
Pattern-matching customer requests
Reasoning insights
Knowledge compounding
Integration with observability platforms
Supported by
Blackbird VCBlackbird VC GiantsUniversity of Sydney Genesis

The problem

Most AI agent builders only learn who they’re losing to and what product feature to build next when a customer says so out loud.

AgenticLib keeps you ahead.

01

We track what's shipping.

We scan your competitors: what's launched, what's changed in the market, what's new in your category, so you're not finding out from a lost deal.

02

We show you the gap, with proof.

Not a vague score. The actual evidence. What Claude and ChatGPT say about you versus them, and exactly which feature is costing you.

03

We tell you what to build next.

A prioritised 3-month roadmap of what to build, plus a clear path to becoming LLM-visible in the use cases you want to own, so you compete where your buyers are already asking.

The solution

Sage AI Platform

One Platform. Product feature, Use case, Competitive landscape overviews for AI Agent Builders. All angles covered.

1/3

Never miss a competitor's move

Sage continuously monitors top AI agent brands by use case and detects changes across competitor websites the moment they happen, alerting you the instant a rival ships or upgrades a product feature.

Coming Soon
2/3

Feature scores by use case, straight from what LLMs say

Every product feature gets a score per use case, built from live queries across Claude and GPT. No analyst opinion. Click any score to read the exact quotes that produced it.

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3/3

Benchmark your position in the market

Benchmark your brand against the competitive landscape, then get a prioritised roadmap of the features and use cases to improve over the next 3 months — so LLMs start surfacing you in the conversations where buyers are already asking.

Coming Soon

WHAT YOU GET

Our analytics lead you to successful outcomes

01

Product Feature Analytics

Know exactly which features get you cited. Scored across security, integrations, pricing, and capability — mapped to the use case that matters for your buyers.

02

Brand & Use Case Benchmarking

See where rivals outrank you — and why. Share of voice by use case, who owns each buying moment, and what it takes to close the gap.

03

Sentiment & Brand Coverage

Hear what LLMs actually say about you. The words, tone, and tags used when a buyer asks Claude or GPT to recommend an agent like yours.

04

LLM Visibility Playbook

Your playbook for getting cited by Claude and GPT. Grounded in what's actually indexed today through citations supported by Parallel.ai.

05

Competitive Intelligence

Continuously monitor hundreds of competitor websites, detect changes over time, and automatically alert users when a competitor ships a product feature.

+47%

Got Questions?

Frequently Asked Questions

AgenticLib is a product management platform for AI agent builders that combines market insights, buyer intent and competitor signals to advise on product feature roadmap decisions.

Most AI agent builders are making product feature decisions without real visibility into what competitors are shipping, which use cases they're winning or losing in, or what buyers are actually comparing before they make contact. Piecing that together manually from lost-deal notes, Slack screenshots, and customer calls is slow, manual, and almost no one actually does it consistently. AgenticLib solves this through product and marketing intelligence.

Sage AI is AgenticLib's platform for AI agent builders. Pick your business domain and instantly see the top competitor brands, filterable by use case, with in-depth product feature scores across security, technical capability, and pricing, each backed by evidence from Claude and GPT. Sage AI currently gives builders insight into their competitive landscape and market insights. Live competitor shipment feeds, benchmarking analysis, and buyer-intent signals are coming next.

We currently cover three types of domains. Vertical domains are industry-specific - think skincare, insurance, or legal. Horizontal domains are cross-industry functions like sales, marketing, or HR. Tech domains are capability-specific categories like AI video creation or voice AI. If your domain isn't listed yet, you can request it - we'll build a customised report for your domain.

Every report covers product feature scores across security, integrations, pricing, and capability, brand and use case benchmarking to show you who owns each buying moment and why, sentiment analysis of how LLMs actually describe brands, and an LLM visibility playbook with citations and data collected from Parallel AI with actionable steps on exactly how to get LLM visible. On top of that, each report calls out the top 3 product features you should build next: specific, evidence-backed recommendations to close the gap on competitors and win the use cases your buyers care about.

We're building toward fully automating the manual process of piecing together messy customer, market, and competitor signals, through integrations with the tools builders already use, like Granola for meeting notes and CRMs like Attio or HubSpot where lost-deal context actually lives. The idea is to connect a builder's own product context directly with our taxonomy of product features, so AgenticLib can continuously advise on what to build next and which use case clusters to expand into. We're also working on doing the content fix ourselves, so builders don't just get told what to publish, we actually do it for them to get them LLM-visible in the areas where their buyers are already asking.