AI agents are here — Are you ready to manage them?

AI agents capable of complex work aren't coming — they're already here. In fact, 79% of companies are actively adopting them, according to PwC. But adoption isn't the differentiator. The teams pulling ahead are building the right foundation first: shared visibility between humans and agents, access to the right systems, clear guardrails around decision-making, and feedback loops that drive continuous improvement.

Without these operational elements in place, no one succeeds — not the agents, not the teams deploying them. This isn't a numbers game or another AI arms race. Winning in the age of agents means being prepared to manage them well.

Why companies need to get AI agent management right 

AI adoption rarely starts from the top-down, beyond high-level leadership mandates to learn, adopt, and experiment with the technology. Rather, it starts with individual teams solving immediate problems: a recruiter builds an agent to screen resumes; finance automates invoice reviews; legal experiments with contract summarization; customer success drafts renewal briefs. Before you know it, dozens of agents are quietly operating across the business.

That worked for the first wave of AI. This next wave is different. As agents move from experimentation to execution, companies need to know how they operate, how they connect, and how they’re governed. Agent management isn’t about slowing teams down — it’s what ensures agents deliver consistent, reliable outcomes across the business.

Right now, most agents are built outside any shared model. Teams don’t fully know what data agents can access, which systems they touch, how decisions are made, or where outputs live. The result is a new kind of operational sprawl — one where “shadow tools” don’t just store information, they take action.

Without a shared layer for visibility and control, the cracks show quickly:

The operational challenge is two-fold: the proliferation and the lack of a unifying operating layer to manage digital teammates at scale. Left unchecked, agent sprawl turns promising automation into fragmented operations — and you guessed it, more silos. 

What’s an AI agent management platform?

An AI agent management platform is the operational layer that gives your business what's called a system of record for agents.

Think of it as the shared environment where humans and agents work from the same source of truth: the same workflows, data, approvals, performance metrics, and governance rules. Instead of teams building agents that live in disconnected chat threads, they build agents that operate inside a structured system where their work can be monitored, improved, and scaled.

This turns disparate experimentation into repeatable operations. A strong system of record lets teams:

The core idea is simple: if agents are becoming teammates, they should work inside the same operational layer as the rest of the business. That shared layer is what makes collaboration, trust, and scale possible.

8 ways to prepare your teams to become managers of AI agents 

1. Define what gets automated — and what stays human

Before deploying agents, be intentional about where they add the most value. Start with work that is repetitive, rules-based, and highly visible — like document extraction, data enrichment, compliance checks, routing, or structured drafting. These use cases deliver quick wins and make it easy to evaluate quality.

Treat agents like a new hire: begin with well-defined, lower-risk tasks, then expand scope as trust builds. Clearly define the role upfront. That includes what outcomes it owns, which systems it can access, and how success is measured. Vague scope leads to inconsistent output. Clear scope turns agents into real leverage for your team.

2. Build an agent-ready data foundations

Agents are only as good as the context you give them. If they’re working off pasted prompts, stale exports, or disconnected docs, you’ll get surface-level output every time.

Treat agent onboarding thoughtfully: connect agents to live, structured operational data — your workflows, systems, and business logic. In Airtable, this looks like clean fields, consistent naming, and explicit relationships between data. When agents can reason across your actual source of truth, their outputs become specific, reliable, and usable. The difference isn’t the model. It’s the context.

3. Connect agents to the tools that let them execute 

An agent without tool access is just a fast responder. An agent with the right tools becomes an operator. Give agents controlled access to the systems where work actually happens so they can take action, not just suggest it. That includes the ability to:

This is what turns AI from insight into execution, moving work forward, not just commenting on it.

4. Teach them your way of doing things (skills)

Your agents shouldn’t be figuring things out from scratch every time. Capture how your team actually operates, including your frameworks, preferred outputs, approval paths, and decision logic, and turn them into reusable skills.

Package these as standardized workflows or instructions that any agent can apply. This ensures every output follows the same playbook without constant re prompting. Over time, agents stop acting like generic assistants and start producing work that reflects how your organization actually operates.

5. Calibrate autonomy with clear guardrails 

Autonomy is not all or nothing. It is something you design. High risk work like legal approvals, sensitive customer communications, and financial decisions should always include human review. Routine, repeatable tasks can run independently, with exceptions surfaced when needed.

Start with tighter control and expand scope as performance proves out. Define where each workflow sits on the autonomy spectrum, set clear checkpoints, and let real results guide how much responsibility agents take on over time.

6. Own the governance layer

Agents execute. You define the rules they operate within.

Set clear boundaries for what processes agents can act on, what data they can access, and where human judgment is required. Build in audit trails, permissions, and escalation paths upfront so decisions are visible and controllable. Even when agents are doing the work, accountability stays with you.

7. Close the feedback loop

High-performing agents improve because feedback doesn't get lost. Every correction, escalation, and refinement should feed back into the system — not disappear into a one-off conversation. Use evaluation rubrics to assess quality consistently across runs, and treat every output as performance data. That closed loop — work, review, feedback, improvement — is what turns agents into compounding organizational assets.

8. Measure outcomes, not activity

Focus on what the agent delivers for the business.

Track improvements in cycle time, capacity, throughput, and SLA performance rather than requests processed or credits used. When an agent consistently drives measurable impact, that is your signal to expand its scope and reduce unnecessary oversight.

What makes an effective agent manager?

The best agent managers blend operational rigor with people-management instincts. They know that scaling agents is less about prompt-writing and more about designing a repeatable operating model. Just as people- and team management is a skill in itself, so is training and evaluating an agent.

Effective agent managers typically:

In practice, the role often sits at the intersection of operations, systems, and team leadership.

The future of AI teamwork needs a system of record

As agents become embedded in everyday work, the challenge shifts from deployment to management. The teams pulling ahead aren’t just using more AI, they’re creating an operating model where agents can be onboarded, governed, measured, and improved like any other high-performing teammate.

That’s where Airtable stands apart. By serving as the shared system of record for humans and AI agents, Airtable gives teams one operational layer to collaborate, manage performance, govern workflows, and scale what works. Instead of agent sprawl, you get coordinated AI teamwork that compounds in value over time.

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