For decades, analytics has been a human story. We built dashboards and reports to help people make decisions: polished charts to find trends, summaries to guide strategy, and visualizations that turned raw data into something humans could digest. Business Intelligence was built to bridge the gap between numbers and human intuition.

Not b2b, not b2c, but b2a: Business-to-agent.

We are looking for startups that are building products where AI agents are the intended customer.

Make something agents want. - Dalton Caldwell - Src

“Make something people want” has long been the mantra of startups. But what happens when humans aren’t the ones making decisions anymore?

We’re at the edge of a shift so big most people haven’t even realized it’s happening yet. The next frontier of analytics isn’t B2B (business-to-business) or B2C (business-to-consumer). It’s B2A: Business-to-Agent. In this world, AI agents (not people) are the primary consumers of data. And that changes everything.

In the B2A world, startups aren’t building data products for users who click, scroll, or interpret. They’re building data apps for agents that consume, process, and act—all without a single human ever needing to look at a dashboard.

Why traditional BI is dead in B2A

The analytics tools we’ve spent decades perfecting are fundamentally broken for this new world. Here’s why:

• Dashboards are designed for periodic review. Daily, weekly, monthly check-ins where humans sit down and think through the data. But AI agents don’t work on human schedules. They operate instantaneously, making decisions faster than any human could comprehend.

• Humans need visualizations to spot patterns, but AI agents don’t need to see data the same way. A beautifully designed chart might impress your manager in a quarterly review, but an AI doesn’t care. It needs data structured in a way it can parse and act on instantly. In B2A, visualizations just aren’t that important.

• Most BI tools generate outputs that are proprietary or tailored for specific visualization environments (e.g., Tableau dashboards or Looker reports). These aren’t standardized formats AI agents can universally integrate with. AI systems thrive on standardized, API-accessible, machine-readable outputs that can be easily parsed, regardless of the downstream system. In B2A, data needs to move seamlessly between systems, and traditional BI’s lack of interoperability makes that impossible.

The problem with human-centric analytics

Imagine trying to operate a self-driving car by feeding it a PowerPoint presentation on road safety. You could include charts on average braking distances, graphs of historical accident data, and even annotated maps of common traffic patterns. But none of that would help the car safely navigate real-time traffic. Why? Because the car doesn’t need summaries or visualizations, it needs continuous, real-time sensor data from LiDAR, cameras, and GPS to make decisions on the fly.

Now apply that logic to AI agents in business. Traditional analytics tools are designed to summarize, visualize, and simplify complex data so humans can understand it and make decisions. A manager looks at a dashboard, sees that sales dipped last week, and decides to adjust marketing spend.

So, what replaces dashboards and reports? Data apps designed for AI agents.

These are machine-optimized systems that deliver structured, actionable insights directly to AI agents. Imagine every business process, pricing, logistics, customer service, being driven by AI agents that consume these data apps in real-time. No dashboards. No reports. Just machine-to-machine analytics.

In this future, the question isn’t “What insights can we visualize?” It’s “What data structures can we build to help agents make better decisions, faster?”

What analytics for agents could look like

If not dashboards, what? Well, we think it will be a combination of structured APIs, real-time events, semantic data feeds, and knowledge graphs, all designed to enable agents to act autonomously and adaptively. Agents, in particular, thrive on context-rich, flexible data that allows them to interpret nuances, handle ambiguity, and make decisions that aren’t just algorithmic but context-aware.

Data Feeds. Structured, Queryable Context

Agents excel at interpreting semi-structured natural language. This means you can feed them narrative-style data summaries or annotated logs instead of rigid, purely numerical formats. Rather than presenting a sales manager with a dashboard that shows regional sales trends, an LLM agent might receive:

"Sales in the Northeast region dropped by 12% last quarter, largely due to supply chain disruptions from Vendor X. However, the Midwest region saw a 5% increase, driven by the launch of Product Y. Customer feedback indicates a growing interest in eco-friendly packaging."

Knowledge Graphs for Relationship-Centric Analytics

Instead of static data tables or charts, agents interact with knowledge graphs to understand relationships and dependencies in complex datasets. In healthcare analytics, an agent might map:

Patient → Diagnosis: Hypertension

Hypertension → Risk Factor: Obesity

Obesity → Treatment: Lifestyle changes

Lifestyle changes → Outcome: Blood pressure reduced by 15%

Data Embeddings for Semantic Analytics

Much of the world’s data is unstructured: customer reviews, support tickets, social media posts. How do AI agents make sense of this? Data embeddings. This technique transforms unstructured text into high-dimensional vectors that capture the semantic meaning behind words and phrases. In simple terms, embeddings help AI agents understand the relationships between pieces of information, even when they’re not explicitly connected.

Imagine you’re managing a product like Noise-Cancelling Headphones. Traditionally, a product manager would analyze a dashboard filled with word clouds, sentiment scores, and charts to understand what customers are saying. But an LLM agent doesn’t need those visuals. Instead, it processes thousands of customer reviews and generates structured insights:

{

"product": "Noise-Cancelling Headphones X",

"semantic_clusters": [

{

"topic": "Battery Life",

"sentiment": "Negative",

"common_phrases": [

"battery drains quickly",

"doesn't last long on a single charge",

"needs frequent charging"

],

"embedding_similarity_score": 0.92

},

{

"topic": "Sound Quality",

"sentiment": "Positive",

"common_phrases": [

"crystal clear audio",

"deep bass and balanced mids",

"immersive sound experience"

],

"embedding_similarity_score": 0.88

}

],

"actionable_insight": "Battery issues are the top negative sentiment driver (92% similarity across reviews). Prioritize battery improvements in the next product iteration."

}

Without ever looking at a chart, the agent understands that battery life is the key issue and can even recommend solutions. No dashboards required.

Why This Matters Now

There’s this quiet moment in the life of every technology when it becomes clear the old tools don’t fit the new reality anymore. At first, it’s subtle. The tools still work (technically) but they start to feel slow, clunky, out of step with how the world is changing. And then one day, almost without realizing it, you look around and see that everything has shifted.

AI is quietly shifting the landscape, making traditional human-centric tools obsolete. If you’re building analytics tools today and still only focused on dashboards and visualizations, you’re solving the wrong problem. The next wave of startups won’t just be about helping people understand data. They’ll be about building systems that let AI agents make decisions autonomously.

Welcome to B2A. For the agents, by the agents.

Try Preswald today!

https://github.com/StructuredLabs/preswald