Seeing is knowing — 3 agent observability best practices for reliable AI

The promise of AI is that things are made easy. Workflows are handled at scale. The technology is so smart that it knows what to do.

That read is not inaccurate, but it’s also not complete. What is true is that it’s never before been possible to mine your rich customer data at record-breaking speed. But it’s also true that AI can make mistakes, especially in the early stages, just as humans do. It takes time to build trust, whether in your technology or with people. And what helps to build trust is consistent, clear communication and transparency — transparency being the key word here.

AI agents need to be supervised, which Deloitte lays out clearly in its December 2025 report on agent impact within operations report. According to Deloitte’s research, 39% of senior leaders are already directing investments toward agentic AI as part of their new technology strategies. Yet this investment requires human oversight — which, in technical terms, equates to agent observability. In this article, we’ll unpack what observability means in practice and the best practices that help non-technical teams run AI agents to produce reliable outputs.

What is agent observability?

Agent observability provides you with the ability to see what your AI agents are doing, understand the decisions they made, and view the work they produced. By continuously monitoring and tracing agent activity, your team can always intervene in real time or adjust your AI agent management after the fact. 

Observability relies on three core capabilities:

Together, these create the conditions for running agents responsibly at scale. 

Why leaders need to think about agent observability

It’s important that leaders don’t mistake basic monitoring for true observability — and to know that not all solutions provide observability. Your teams need to do more than simply confirm whether agents are running or have completed a task; they need the ability to investigate unexpected behavior, identify root causes, and improve the system over time. 

This becomes critical as employees increasingly move beyond using AI for individual use and deploy agents across business-critical workflows that benefit the whole team — allowing agents to do things like update records, send communications, route approvals, trigger purchasing decisions, or generate customer-facing content. In these cases, a misconfigured agent or a degraded data source can create errors across systems before anyone notices. Agent behavior becomes a governance and accountability concern, and one that lives at the leadership level.

That may sound risky, but there is much to be gained. For example, Deloitte found that marketing leaders who adopt automation report 29% higher revenue impact from their content marketing efforts. Teams can achieve these kinds of boosts when they deploy agents with proper governance. And because observability allows teams to step in and make adjustments, they can optimize agent performance against specific objectives.

3 best practices for agent observability

The overarching best practice for agent observability is that this is never something you overlook. Every great leader knows that their teams need breathing space, but also clear check-in points.

Build in observability from the beginning

Observability is not something to add after agents are already built. Begin by thinking about the audit trail you’ll need to have in place to debug failures or unexpected outcomes before you actually need it. This looks like: agent action logs (what agents did), decision timestamps (when they did it), and input-output chains (what data they used and acted on).

It’s a good idea to create structured audit fields in a system of record to capture agent activity across systems — for example: "Last Agent Action," "Action Timestamp," "Updated By". Maintain and regularly review a dedicated operations log of agent activity that's searchable, filterable, and links back to the records involved, and look for platforms that make this process easy.

Know what “good” looks like before you deploy

Observability requires knowing what you're looking for. Without defined success criteria, you can have full visibility into an agent's actions but struggle to know whether agents are performing well. As the Deloitte report notes, your KPI(s) should be your North Star as you design your agentic systems — well before implementing them. High-level, your overarching goals are probably similar to pre-AI. Higher customer success and retention, for example. But when you consider that each agent in a multi-agent system is assigned a discrete task, then it follows that there are specific, more granular ways to measure its performance. 

Deloitte recommends mapping a framework that looks at each agents’ cost (whether that’s in tokens or human review time), speed (is there latency or bottlenecks?), productivity (what is the agent’s success rate vs. need for human correction), quality (of outputs or decision making about tool access and data retrieval), and trust (don’t leave out anecdotal feedback from your team). The most important thing is that you define what you’re looking for in the data.

Designate the right human checkpoints

Monitoring performance also means monitoring risk. Agents are working with fluid, real-time data, which makes human checkpoints important in any workflow where the cost of an error is high. Naturally, requiring humans to approve every action defeats the purpose of automation, but consider key inflection points where a wrong decision might have a damaging consequence.

In Airtable, teams can structure agent workflows with explicit approval steps, using an "Agent Recommendation" field, an "Approved" checkbox, an "Override" option, so you can oversee what the agent proposes and what the human decides, at least until the team has enough confidence to fully automate. This graduated approach helps to establish trust in agent automation.

Core components of the agentic observability stack

For marketing and operations teams, observability doesn't require a separate technical system or observability tool. Instead, it requires that the platform where agents work provides visibility by default. You’ll need:

Why observability matters even more in multi-agent systems 

Tracing a single agent’s actions is fairly straightforward, but things become more complicated when multiple agents work across multiple workflows. 

In a multi-agent workflow, agents hand off work to other agents once their task completes. This means that if the first or second agent makes a mistake, the next agent runs with it, and errors can compound or lead to an unexpected outcome. IDC research found that 97% of enterprises struggle to scale agents, in part due to fragmented visibility. Ideally, you aren’t adding a new stack of monitoring tools, but are instead choosing and utilizing platforms that can serve as a single source of truth and provide built-in observability. 

Airtable provides a shared view into your work(flows)

Airtable provides a shared operational surface where humans and agents collaborate, using the same data, accessing the same views. And with

Airtable Hyperagent

, teams get audit trails, human approval workflows, and structured governance built in from the start, so you can be a frontrunner in implementing agentic AI across the workflows that add value without losing visibility or control.