System of records: The key ingredient to AI agent success in 2026

Forward-thinking teams build for the future needs of the business, and what you build today, agents will run tomorrow. This is the mindset leaders need to begin with, thinking beyond what AI can do today to prepare for an imminent agent-first future. 

The rapid innovation in frontier AI models mean that current points of failure generally aren’t related to model intelligence; instead, it’s about how agents are deployed into existing workflows and how your team works with them. That tends to be on an individual level, where teams are building siloed context and knowledge that doesn’t connect to the wider organization — and doesn’t lead meaningful business outcomes. But the challenge and solution are the same: collaboration. Humans and agents must work across the same operational surface, same data, and be held accountable to the same outcomes. Teams that bridge this gap now are sure to have a competitive advantage later.

So, the question is simple: How does your team work with agents today, and what should they be doing next to build trusted agent workflows with governance, approvals, and human accountability? Fortunately, the answer is also simple: Establish a central agent system of record.

What is an agent system of record?

Many teams have already started working with AI agents, but the adoption tends to be fragmented with each person or team creating their own personalized context, feeding it only the information relevant to their role. When agents operate in isolation, context gets siloed outside the organization. Insights aren't captured and shared, outputs go unverified, and decisions get made without a common foundation. Over time, this leads to agent sprawl, a growing tangle of disconnected tools that's hard to govern and even harder to untangle.

An agent system of record provides a central source of truth that both humans and agents work from — it’s a shared operational layer that includes real-time data, business context, policies, and workflow state from across the organization. It's not just a place where data lives. It's the environment where agents and humans work side by side, with full visibility into what's happening and why. The system accumulates knowledge over time, making it available to anyone — or any agent — in the organization. Think of it less as a repository and more as the surface where human and agent knowledge and expertise builds over time, so that improvements and context are available across the business.

5 reasons your agents need a system of record 

1. Better agent outputs start with structured data and context 

Many teams have already built a central source of truth for their people. eBay, for example, consolidates and tags customer feedback from surveys, support tickets, forums, and social media in Airtable so teams can act on signals faster. Or, MGA Entertainment uses Airtable to connect marketing workflows, organizing over 40,000 assets and surfacing real-time campaign data. And when that same shared view is available for your agents, the quality of their output changes entirely. 

An agent operating from prompt histories and one-off documents has to fill in some blanks, often guessing at details or direction. However, an agent operating within a structured system of record — with explicit relationships, current state, and business logic — reasons across the actual reality of your operations, taking in valuable context it can’t otherwise know. This is the same detailed, nuanced operational reality that humans see and work from. It makes the difference between a generic deliverable and one that reflects your business’s voice and tone, current customer references, and real data.

2. Conversations need to become real workflows 

Modern agents don't just answer questions and brainstorm ideas. They update records, trigger workflows, assign tasks, and send communications. That requires somewhere to write back to, not just read from. A system of record gives agents structure they can immediately act on. Instead of reviewing outputs and figuring out how to incorporate them into your workflows, the work lands exactly where it needs to be.

For example, ask your AI agent to brainstorm 50 content ideas and write the strongest ones directly to Airtable — with owners, deadlines, and status fields. The chat conversation becomes a functioning workflow before you've closed the browser tab.

3. Enterprises can't trust what they can't observe

Chances are you’d never hand off something business critical to an employee, however skilled, and not check in. The problem with AI agents that organizations face today is that they can’t check in — they don’t know what the agents are doing or how they made decisions, and this puts them at risk. As a result, some organizations won’t deploy agents at all, while others deploy without oversight and hope for the best. But it doesn’t need to be this way. 

A system of record like Airtable provides the governance layer: defined boundaries, audit trails, role-based access, and visibility into agent decisions. With this in place, companies can deploy agents with confidence, verify their reasoning, and step in when needed. 

4. Agents need somewhere to compound knowledge over time

AI always continues to learn, but the question is whether and how much your organization stands to benefit. Airtable CEO Howie Liu put it best: “The models will keep getting smarter on their own. What matters now is the system that lets agents learn, compound, and scale.” Organizations need agents to build on every interaction, correction, and workflow so that the entire operation becomes more capable over time. That only happens if there's a persistent, structured place where agents work, and where decisions and context are stored and built upon — not reset with every session. 

A system of record makes this possible. When agents log actions and outcomes back into a shared space, teams can review, guide, and refine what's working. Insights compound. Performance improves. Nothing gets lost.

It also becomes the foundation for a library of your team's best agent skills — top-performing prompts, proven instructions, workflow templates, organized by use case and owner. Instead of starting from scratch, every team member pulls from the same tested playbook.

5. Soon there will be more agents than humans in workflows — and they'll need to collaborate with each other

The gap between teams experimenting with agents and those running coordinated, multi-agent workflows at scale is already widening. The difference isn’t better models — it’s having a shared operational surface where agents can build on each other’s work. One agent researches, another classifies, a third executes. Without that coordination layer, agents fall into the same traps as humans: siloed work, lost context, and misalignment.

A system of record is what makes true collaboration possible. It acts as the memory and coordination layer across every agent and team. For example, a product manager works in Claude Code, a researcher captures feedback in Perplexity, a marketer builds campaigns in Cowork, and an exec checks progress in ChatGPT. While the tools are different tools, the underlying system is the same. Work flows back into a shared source of truth where context is preserved, progress is visible, and every agent operates with full awareness of what’s already been done.

The result is work that actually compounds. Marketing reflects real customer feedback. Status updates reflect what’s happening on the ground. And instead of disconnected outputs, you get a system where agents (and humans) operate as one coordinated team.

Build an agent system of record with Airtable 

Agents are only as capable as the system they work within. Without structured context, shared visibility, and governance, even the most capable agents produce work that can't be trusted, built upon, or scaled across business-critical functions. Airtable is built to be that system — a shared operational surface where humans and agents work side by side on the same data, toward the same goals, with the observability enterprises need to deploy AI in production with confidence. Whether agents access Airtable directly or connect through Model Context Protocol (MCP), the collaboration layer for humans and agents remains the same.

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