The Rise of Agentic AI for Software Building: How Systems of Action Are Replacing Systems of Record

Lovable AI's meteoric rise and Airtable's recent AI product launches aren't just cool features—they're signals of a fundamental shift in how we think about application software. The traditional assumptions about fixed schemas and implementation ecosystems are being upended.

Context now trumps schema in almost every way. The dynamics of buying, deploying, and extracting value from application software are forever changed. At Bonfire, this isn’t just a trend—we’re helping build the next generation of agentic AI companies that will define the new software landscape.

First: What is Agentic AI?

Agentic AI is the evolution of software from passive tool to proactive doer. It’s not just about “AI assistants” answering questions or surfacing insights. It’s about AI agents—digital workers that take initiative, operate autonomously within defined boundaries, and deliver real outcomes on your behalf.

Agentic AI means your software isn’t just waiting for instructions; it’s actively handling tasks, orchestrating workflows, learning your business context, and getting things done without handholding. It bridges the gap between traditional automation (static, rule-based) and full-on intelligence (dynamic, context-aware, self-improving).

AI assistants were the warmup act: helpful, but ultimately reactive. Agentic AI agents are the main event: software with real agency, trusted to act in the background and move the needle.

Agentic AI Examples

The wave is already here—at Bonfire, we’re proud to have invested in some of the B2B software teams shaping what’s possible:

• ​Octave: Building a "messaging marketing brain" to ensure organizations' go-to-market messaging is on point and effective, especially as products and competitors evolve rapidly in an AI-driven world.
• ​Abstract: An AI platform that synthesizes millions of legislative and regulatory data points, uncovering nuanced connections and implications often missed in manual review. Abstract's AI sets a new standard for regulatory intelligence.

When you give software true agency, vertical pain points melt away, workflows compress, and companies operate at a fundamentally new speed.

So, Does Metadata Even Matter Anymore?

Previously, metadata was seen as a moat—more metadata meant more context for your agentic assistant, leading to better AI and a defensible advantage. But with the emergence of Model Context Protocol (MCP) and other LLM-native integration services, any system of action can easily access metadata from any system of record. Carefully curated data structures and relationships are now commoditized APIs.

The new delineation: Systems of Records may have interesting metadata, but Systems of Actions actually get things done. Your metadata isn't your moat—your ability to act on it is.

The Breakneck Speed of AI's Evolution: Surpassing Agentic AI

We've witnessed the fastest transformation in software interfaces in IT history:

• ​Phase 1: AI Helps Us Use Software (The Co-Pilot Phase): AI assists, answers questions, and completes tasks.
• ​Phase 2: AI Does the Work (The Agentic Phase): AI agents perform entire roles with minimal guidance.
• ​Phase 3: AI Creates the Software (The Vibe Coding Phase): AI enables rapid creation of new product modules, bypassing traditional engineering bottlenecks.

How Does Agentic AI Work?

Agentic AI combines large language models (LLMs), structured workflows, and dynamic context ingestion to automate complex, cross-functional work. It:

• ​Ingests context: Pulls in real-time data, documents, and company-specific processes.
• ​Plans and decides: Reasons about goals, constraints, and potential actions, choosing the best next step.
• ​Acts autonomously: Executes workflows, sends messages, updates records, and surfaces exceptions or approvals as needed.
• ​Learns and adapts: Improves with every interaction through feedback and learning.

The Death of the "We Bring Schema, You Hire Armies" Industrial Complex

Historically, software companies provided a database schema and required customers to hire consultants and admins to customize the product to their specific needs. This led to massive spending on professional services and operations just to insert company context into software.

The new reality in software-building with AI

With the right schema, administrative capabilities, and agentic interfaces, customers shouldn't need expensive systems integrators. Deployment agents can ask questions, recommend setups, and deploy configurations automatically. New capabilities can be introduced and deployed seamlessly.

Extra credit goes to companies that help customers set up workflows, recommend standard processes, review existing ones, and connect them to process experts.

The Benefits of AI for Building Contextualized Software

We're witnessing the demise of "one-size-fits-all" software and the emergence of infinitely personalized applications. AI-powered platforms will mold themselves to fit each customer's unique context, dynamically generating the exact schema, workflows, and interfaces needed.

A construction company and a law firm buying the same "project management" platform will receive completely different applications, each optimized for their specific needs.

Dynamic Schema Generation

Software analyzes the customer's website, SIC codes, documents, and existing systems to recommend the right schema on the fly.

AI-Driven Process Optimization

Software recommends business processes that reflect the customer's operations and best practices from similar companies.

Predictive Analytics and Auto-Optimization

Software recommends key questions to ask, provides answers, and can fix underlying processes or send instructions to other systems with approval.

On-Demand Feature Creation

Users can request new features, and the software builds them based on existing context, eliminating the need for external integrations or new vendors.

Purpose-Built Interfaces

User interfaces are automatically generated for each task, optimized for how users think and work, rather than predetermined screens.

This world is already emerging in smaller apps and is coming for every category of application software. The framework of horizontal vs. vertical software is collapsing, replaced by contextualized software tailored for each customer.

To deliver truly contextualized software, companies must start within a given domain to translate customer needs into appropriate schema and workflows, then expand from there. Deep domain expertise is essential.

Implications of Agentic AI for Founders and the Industry

Several high-impact shifts are underway:

1. Implementation and the SI Industry Extinction

Onboarding and implementation are becoming automated. Systems integrators must evolve to provide context around business processes or risk obsolescence.

2. System of Records Without Actions = Irrelevance

Applications without a powerful system of action layer are becoming obsolete. Data without action is just expensive storage.

3. Systems of Actions Can Eat Systems of Records

Systems of actions can become systems of records if needed, integrating and eventually replacing traditional systems of record.

4. Horizontal Software: Easier & Harder

It's harder for horizontal software companies to achieve breakout success without domain focus, but easier to personalize apps and expand horizontally once established.

5. Vertical Software: Advantage Amplified or Lost

Vertical companies must double down on frictionless setup, flexible schema building, recommended processes, and agent-based analytics. Otherwise, they risk being replaced by new vertical or adaptable horizontal companies.

6. When Reinforcement Learning Comes: Pounce

The ultimate moat will be proprietary know-how, not just data. Software that accumulates intelligence about what drives success in each industry will have a significant advantage.

The bottom line on agentic AI for software-building

Agentic AI is a foundational shift. Every breakout software company of the next decade will be context-first, agentic, and outcome-driven.

If you’re building software that does work—not just stores data—or you’re thinking about how agentic AI changes your category, consider how these trends will shape the future of software.