Startup Scouting
3 conversational turns exploring this topic
Q: What hidden gems are solving interesting problems in AI?
Subject: The Agentic AI Mirage: What's Really Solving Problems
Friends,
The AI hype machine is in overdrive, and this week's deluge of articles (1,800+) proves it. But let's cut through the noise. Everyone's screaming about "Agentic AI," but I'm seeing a dangerous disconnect. The real hidden gems aren't building smarter agents; they're fixing the foundational mess that prevents those agents from being useful in the first place.
The Agentic AI Mirage
The data is clear: "Agentic AI" is the buzzword du jour. We're seeing a ton of solutions promising custom agentic workflows, secure configurations, and even entire platforms like "Opus" touting agentic capabilities. But here's the brutal truth: these solutions are trying to build a skyscraper on a swamp.
Why? Because the data governance and integration layers needed to fuel these agents are fundamentally broken.
The Real Hidden Gems: Shoring Up the Foundation
While everyone is distracted by the shiny object of "Agentic AI", the real innovation is happening in the unsexy world of data infrastructure. We need to focus on the prerequisites for AI to be effective.
| Problem | Solution Category | Examples |
|---|---|---|
| Siloed, Ungoverned Data | Data Products & Metadata Governance | Snowflake, Databricks, emerging data mesh solutions |
| Lack of Integration | Unified Data Platforms | Companies building end-to-end data pipelines |
| Missing Context | Knowledge Graphs & Semantic Layers | (Still nascent, but crucial) |
My Hot Take:
Agentic AI will be transformative, but not until we solve the data mess. Right now, most "agentic" solutions are just glorified automation scripts running on garbage data. They're expensive glorified automation scripts.
The Elephant in the Room: Data Governance
Look at the data: articles mentioning "Data Governance" are consistently linked to solutions like data products, AI agents, and metadata governance. But there's a second "Data Governance" gap in the data, linked to things like software updates and customer service. This tells me two things:
- We're using the term "Data Governance" too broadly, diluting its meaning.
- The real challenge isn't just having data governance, but operationalizing it and making it relevant to business outcomes.
The Opportunity:
The companies that will win in the AI era aren't the ones building the flashiest agents. They're the ones providing the plumbing that makes those agents possible. Think Snowflake and Databricks, but also the smaller players building innovative data mesh architectures and knowledge graph solutions.
Advice for Data Professionals:
- Be skeptical of "Agentic AI" demos. Ask the hard questions about data lineage, governance, and integration.
- Invest in your data foundation. Focus on building robust data pipelines, implementing strong data governance policies, and creating a single source of truth.
- Learn about data products and data meshes. These are the architectural patterns that will enable you to scale your data efforts and deliver real business value.
Until next time, keep digging beneath the surface.
Yves Mulkers
Ins7ghts
Q: Tell me about the first one
Subject: Re: The Agentic AI Mirage: What's Really Solving Problems
Friends,
The AI hype machine is in overdrive, and this week's deluge of articles (1,800+) proves it. But let's cut through the noise.
You asked about the first hidden gem. Here's the truth: The most impactful "AI" isn't about flashy agents; it's about targeted tools that empower specific workflows.
This week, OpenAI quietly launched Prism, a "free AI workspace for scientists." Ignore the breathless headlines. This is the real story.
Why? Because while everyone's chasing general-purpose AI, OpenAI is building a verticalized solution. They're not trying to replace scientists; they're giving them superpowers.
Here's the breakdown:
| Feature | Agentic AI (The Hype) | Verticalized AI (Prism) |
|---|---|---|
| Goal | Autonomous problem-solving | Enhanced productivity within a specific domain |
| Data | Massive, often unstructured | Curated, domain-specific datasets |
| User | General public, non-experts | Subject matter experts |
| Impact | Potential for disruption, but often theoretical | Immediate, measurable improvements in existing workflows |
| Risk | Hallucinations, bias, lack of explainability | Limited scope, easier to validate and control |
My hot take? Agentic AI is a distraction for 90% of businesses. You don't need a general AI to run your marketing department. You need targeted tools that help your marketers do their jobs better.
Prism is a blueprint. It's not just about slapping an LLM on existing software. It's about deeply understanding a specific workflow and building AI tools that seamlessly integrate.
Here's my advice:
- Stop chasing the "agent" dream. Focus on identifying bottlenecks in your existing workflows.
- Talk to your subject matter experts. What tasks are tedious, time-consuming, or prone to error?
- Look for verticalized AI solutions. Don't try to build everything from scratch. There are likely startups already solving your specific problem.
- Demand explainability. If you can't understand how the AI is making decisions, you can't trust it.
The future of AI isn't about replacing humans; it's about augmenting them. And the companies that understand this will be the ones that thrive.
Stay sharp,
Yves Mulkers
Data Strategist
Ins7ghts
Q: Have they raised funding?
Subject: Re: The Agentic AI Mirage: What's Really Solving Problems
Friends,
The AI hype machine is in overdrive, and this week's deluge of articles (1,800+) proves it. But let's cut through the noise.
You asked about funding. Yes, the usual suspects are hoovering up capital. But focusing solely on who raised money misses the bigger picture. It's about where the smart money is going, and more importantly, why.
The headlines scream "OpenAI seeks $50B in Middle East!" (14 articles this week alone). Okay, fine. But that's table stakes. Everyone expects OpenAI to raise gobs of money. The real story is what that money will be used for, and what problems aren't being solved by simply throwing more compute at the LLM leviathan.
Here's my hot take: "Agentic AI" as currently envisioned is a mirage. We're chasing autonomous systems that can reason and act independently, but we're neglecting the crucial scaffolding needed to make them reliably useful. Think of it like building a skyscraper on a swamp.
| Area of Hype | Reality Check | Funding Follows Hype? |
|---|---|---|
| Autonomous Agents | Still struggling with basic reasoning, hallucination, and lack of common sense. | YES (Overfunded) |
| LLM-Powered Tools | Dependent on prompt engineering, brittle, and require constant human oversight. | YES (Moderately) |
| Data Integration | The real bottleneck. Connecting LLMs to real-world data sources is a fragmented mess. | NO (Undervalued) |
| Explainable AI (XAI) | Crucial for trust and compliance, but often an afterthought. | NO (Undervalued) |
The hidden gems aren't building more agents; they're building the infrastructure that makes agents (and LLMs in general) actually work in the real world. Think about companies tackling:
Robust Data Integration: Connecting LLMs to disparate data sources, cleaning and validating the data, and ensuring data lineage. This is where the real* value lies.
Explainable AI (XAI): Tools that provide transparency into LLM decision-making, allowing us to understand why* an agent made a particular choice. Crucial for regulated industries.
- Prompt Engineering Platforms: Moving beyond ad-hoc prompting to structured, version-controlled, and collaborative prompt development.
My advice? Stop chasing the shiny object of "Agentic AI" and start focusing on the unsexy but essential work of data integration, explainability, and robust tooling. That's where the real problems are being solved, and that's where the smart money should be going.
Until next time,
Yves