Why Observability Matters For Business AI Automation And Agents

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Whether your business already uses AI and workflow automation to streamline your processes, or you’re considering turning manual repetitive workflows into automation within your business – one thing is crucial to implement: observability.

Automation observability is often an overlooked topic when it comes to building automated workflows.

You may engage with an automation builder or freelancer to turn a manual task or process into an automated workflow, but when it goes live – do you really know how its performing, or what decisions are being made?

AI agents and automations are quietly taking over a lot of the “messy middle” and manual work inside businesses.

• They route and qualify leads before your sales team deals with them
• They triage support tickets, answering and freeing up your support team’s time
• They pull data and build reports from CRMs, Google Sheets, job systems and APIs
• They nudge and converse with people by email, WhatsApp, and SMS
• They make micro-decisions all day long that used to sit with humans

And, in a lot of organisations, they’re doing this with very little visibility.

Over 60% of the businesses we’ve spoken to through 2025 that already have live automated workflows have zero visibility on their workflows: no evals, no audits, no performance dashboards, no tracing and no error logs.

Typically, what we find in these cases is that the automations and workflows either sit inside a suppliers (freelancer, agency or builder) workflow platform, or someone within the business has built them – yet the rest of the business cannot clearly explain what is actually going on.

These automations might be solving critical processes within an organisation – and even if they “just run”, or “just work” – if something breaks, it’s often a human noticing a weird outcome after the fact, rather than a system telling you something is wrong.

AI automations or agents are not simply just “macros” running in the background.

They are often core infrastructure performing critical processes within a business – ranging from booking in and qualifying new leads, conversing with prospects via chat or voice agents, generating business intelligence and reporting, or even analysing business critical finances.

If anything goes wrong – who’s going to fix it? how do you know where things broke down?

That’s where observability comes in.

This article is about how to see inside the black box of your AI agents, automated workflows and process automations, how to report on performance in a way that actually improves the system, and how to use that data to stop errors, tighten decision making and stay audit-ready.

Why observability matters for AI-driven operations

An experienced AI automation agency will discuss and design observability by default when developing your business workflows. Whilst traditional monitoring might tell you things like:

• The server is up
• The API is returning 200s
• The queue is empty

This is useful information, but it does not tell you:

• Why your lead qualification agent suddenly stopped booking appointments on Tuesday
• Why your procurement bot is flagging far fewer overcharges this month, despite similar volumes
• Why your AI voice agent is repeating questions all of a sudden
• Why your WhatsApp chatbot isn’t responding to customers suddenly

AI agents and modern automations behave differently from classic web apps:

• They’re probabilistic – there is not always one fixed path
• They’re often composed – an LLM plus tools, plus APIs, plus custom code
• They make decisions that have real commercial and compliance impact

Observability, in this context means:

Having enough structured visibility into what your agents and workflows did, why they did it, and what happened as a result – so you can understand, improve and trust the system.

Without that, you’re effectively running parts of your business on a black box.

Layers of observability for AI agents and automations

To make observability practical, think about it in 3 layers:

• Infrastructure and platform
  • Latency, error rates, timeouts, rate limits, token usage, model errors, failure rates, execution times
• Agent and workflow behaviour
  • Which steps ran, which tools were called, what branches were taken, how many retries or fallbacks were used.
• Business outcomes
  • Did the task actually succeed? Was the lead qualified correctly? Did the supplier get flagged or approved? Did the ticket get resolved? Did the booking succeed?

If you’re only looking at the infrastructure layer, you may see that everything is “green” while an agent is quietly misclassifying or mis-routing half of your work.

If you’re only looking at business outcomes from your automations, you see “something is off”, but have no idea where in the chain it’s going wrong.

You need enough signals at all three layers to draw a line from “this request came in” to “this decision was made, by this version of the system, and it led to this business result”.

With high volume workflows, this becomes even more critical.

A simple API error, a wrong decision, a rate limit, missed data capture can all have significant business consequences and knock on effects if there is little or vague observability within your workflows. Redundancy is another key element to automated workflows: if we have an error in the flow is there another path? if the API goes down what happens? if an LLM responds with a 40x error is there a backup? but this is a whole topic in itself that we’ll write up for another article!

Instrumenting agents: what to capture and where

Let’s talk about what you actually log.

You don’t need to capture every byte of every step forever, that could be a recipe for noise and high storage bills. You do need the right shape of data at key points.

Inside the “agent brain”

For LLM based agents or decision bots – including AI voice agents, chatbots, or automations using agent decision making, useful signals include:

• Decision logs
  • Each time the agent chooses a tool, path or action, you log:
    • Which policy or chain ran
    • Which option was selected
    • Any scores or reasons if available (confidence scores or an explanation if possible)
• Prompt and response telemetry
  • You don’t necessarily need full raw prompts forever, but you should be capturing:
    • Which template or prompt version was used
    • Key variables or values passed into it dynamically
    • High-level metadata (use-case, session or chat id, client, segment)
    • Model outputs in a structured way where possible (e.g. parsed JSON decisions)
• State snapshots at key checkpoints
  • For longer workflows, it helps to log:
    • “Here’s what we knew about this request before making the decision”
    • “Here’s what changed after the decision”
• Correlation IDs
  • Every journey (lead → agent → CRM → followup) should carry a single ID through all systems, so you can reconstruct the story later.

When you come back to debug a strange decision or audit a process, these logs are what you read as a narrative. The ability to join up all signals gives you the ability to quickly hone in on issues before they become serious problems.

In the workflow and automation layer

For tools such as n8n/Make/Zapier/Custom orchestration, aim for:

• A single session or run ID that is unique to the workflow execution
• A clear record of each step starting, succeeding or failing
• Inputs and outputs for critical steps (sanitised where necessary)
• Information about retries
  • Did we retry on error?
  • Did we fallback to a different LLM or to a human?
  • Did we silently drop the task or fail?

You don’t need every minor transformation logged – but you should be able to answer:

“What did this workflow do for this request, step by step, and where did it break?”

At the business and user level

Finally, observability needs to connect to outcomes, not just the mechanics of the workflow automation.

For each key workflow, define:

• What success looks like
  • A lead is qualified and an appointment is booked
  • A ticket is resolved within SLA
  • A supplier overcharge is detected and flagged
• What partial success looks like
  • Data was captured, but a human had to intervene
  • The task was completed, but slowly or with extra back and forth
• What failure looks like
  • Wrong decision made
  • Nothing happened
  • The user abandoned the interaction

Then log those outcomes in a way that can be grouped and reported on – by agent, by client, by campaign, by time period.

Observability in practice – how to implement

At flowio, we build in observability within business workflow automations and AI agents by default for our client partners. This involves planning what we need to know vs what the client needs to know into the workflows.

There are many ways in which we can capture and report on observability through automated workflows, however here are our favourite tools to build in basic observability:

n8n datatables

We build a lot of our client tools, automations and agents within the n8n platform. One feature that makes n8n great for observability is datatables. n8n datatables are small database instances, similar to a spreadsheet that live fully within your n8n instance. This makes them perfect for controlling session states, capturing key execution data and reporting further down the line.

Whilst n8n datatables are a lighter option than other database integrations such as Postgres/Supabase/Airtable/NocoDB – we use n8n datatables extensively for observability within n8n.

Take a simple n8n workflow such as the below.

For illustration purposes – all this workflow does is ask an AI agent to tell us a joke. It has a backup LLM in case there are any issues with OpenAI – and outputs into a set node so we can format the result.

Now, consider what we may want to know from this flow.

• When did the execution start?
• When did the execution finish? Was it successful?
• What was the input?
• What LLM model did we use?
• How many tokens did we use?
• What was the agent output?
• What was the runtime of the workflow?

In this example – we could create a simple n8n datatable with the following:

• sessionId – a unique ID to identify each workflow run
• workflowStart – the datetime of the workflow start
• workflowEnd – the datetime of the workflow finish
• input – the input that has been set
• output – the agent response
• LLMused – the model or fallback that was used
• tokens – the number of tokens we’ve used for the agent response
• runtime – a calculated field of seconds it took for the workflow to run

So, with our n8n datatable, we can now add this in to the workflow to capture this information with each execution:

When the workflow runs, we create a unique session ID to identify the execution and write the session ID and workflowStart to the datatable.

Once we get to the end of the workflow, we merge the table and the output and then update the full session row with additional details.

For every execution of the workflow – this gives us a unique entry that provides us signals of how long the workflow took to run, did any of our backup paths get used, the usage cost, the runtime in seconds – and more importantly the decision or output from the AI agent.

If the workflow failed we will have a start time yet no end time and can pinpoint the exact execution time to understand what went wrong. Of course – in real world automations we may build in fallback paths, and additional error logging to help identify failures and errors.

Although this is a very basic example – it shows you what is possible for tracking key signals in your workflows to help you debug, audit and understand what is going on under the hood.

Using datatables for things like session tracking can also enable more advanced logics within your workflows, enabling switches, if statements and unique paths based on session data.

Design your observability processes carefully as to avoid storing irrelevant, or potentially non compliant data such as personal details, or things like API tokens.

n8n datatables can be a powerful tool to help you debug and understand key points of your workflows. This process is also transferable across platforms, and with any database. We also use hosted tools such as Gotify to keep us alert of uptime monitoring on our flows in the same way.

Observability in practice: how to monitor performance

In a similar methodology – we use relational databases within our workflows to capture and observe performance metrics from automated workflows, voice agents, chatbots and more.

An experienced AI automation agency will provide transparent visibility and reporting for all of your AI agents, automations and chatbots. This should not only be to gauge performance of the automation – but to help improve and optimise agents over time. If automations and AI agents are built in-house, this can often be overlooked due to time constraints – and where the value of an experienced AI automation agency comes in.

For all of our client partners, we provide real-time reporting dashboards that provide key KPI metrics, live automation performance and more importantly sections that allow real-time improvements to agents.

In summary: why observability matters for business AI agents and automation

AI agents and business workflow automations are never perfect, when key business decisions are delegated to AI or code – it’s critical to ensure there is a way to identify any issues quickly. From understanding why an AI agent made a decision, how long each run takes, to how much each execution costs – observability can significantly improve your automations, and the ability to proactively debug issues to avoid costly issues.

If your current AI automation agency hasn’t discussed observability, auditing, error logging or evals with you – it may be time to look further afield for an experienced AI automation agency that can implement observability by design into your flows – and more importantly provide the ability for your business to monitor real-time performance.