Let Agents Read Before They Act: The First Workflow That Builds Trust

• Start with a recognizable job. A Monday performance answer gives teams a concrete way to evaluate an agent’s accuracy, consistency, and usefulness.
• Establish cross-publisher context. Consistent data definitions and retail media expertise help agents produce answers people can use.
• Expand authority deliberately. Reporting, Insights, and Campaign Actions connectors are available now, with an Optimization connector coming soon.
• Define controls for each workflow. Teams should establish ownership, permissions, review requirements, and escalation procedures as agents take on more responsibilities.

Most retail and commerce media teams already have a good candidate for their first agentic workflow. It is the performance question someone has to answer every Monday morning.

Where are we overpacing? Which retailers drove the change in performance? What needs attention before we spend another week’s budget?

Before anyone can answer, someone has to retrieve data from multiple publishers, align reporting periods, reconcile metrics, and assemble a view the team can use. Then the analysis begins. An agent can help prepare that view. The team can inspect what it retrieved, challenge its interpretation, and assess whether the answer is useful enough to rely on next week.

That makes the Monday performance answer a practical first workflow. It addresses a recurring job, produces something people can evaluate, and creates a foundation for broader responsibilities.

The progression starts with a dependable view of performance. From there, teams can use agents to investigate changes, prepare recommendations, and support execution within a defined scope.

Why start with a Monday performance answer?

A recurring reporting workflow gives teams a manageable environment for evaluating an agent’s work.

The assignment can be specific: retrieve last week’s performance across approved publishers, compare it with the previous period, flag pacing exceptions, and prepare a summary for review.

Each run creates an opportunity to check:

• Whether the agent retrieved the correct accounts and reporting period.
• Whether it used consistent metric definitions across publishers.
• Whether the totals reconciled with the source.
• Whether its explanations were supported by the data.
• Whether it identified missing or incomplete information.

These checks help teams understand where the workflow performs reliably and where people still need to intervene.

They also reveal the actual operational value. Preparation time, correction effort, and repeat usage tell a more useful story than a single impressive answer.

Why does cross-publisher normalization matter?

Retail media performance spans multiple publisher environments, each with its own reporting structures, campaign hierarchies, and measurement conventions.

A question such as “Which retailers need attention this week?” requires more than retrieving separate reports. The answer depends on how the data is aligned and interpreted. Without that foundation, an agent may return a polished summary that leaves the team with the same reconciliation work it had before.

Skai’s retail and commerce media infrastructure spans 300+ publishers. Its depth in the category and normalized cross-publisher data provide a foundation for workflows operating across those environments.

For the marketer, the practical benefit is continuity: the same business question can guide the workflow across publishers, supported by consistent definitions and an understanding of how retail media operates. That foundation becomes increasingly important as agents move from retrieving information into explaining performance and preparing the next move.

How did Matrixx CPG change the start of its week?

At Matrixx CPG, Monday reporting required account teams to assemble pacing, budget, and performance information across client accounts. They moved through brands and retailers, filtered campaigns and keywords, and transferred information into Asana before adding analysis and deciding what to do next.

Matrixx connected Claude to Skai through the Model Context Protocol, or MCP, using Skai’s Reporting Connector. The agency then built reusable Claude Skills to prepare campaign performance and budget recaps and deliver them through Asana before the workweek began.

The change was in how account managers started their week. With reporting prepared in advance, they could begin by reviewing what had changed and deciding what deserved attention. Matrixx also described greater flexibility in custom reporting and client requests, alongside reduced friction in training employees and tagging data.

That is a useful model for a Monday performance answer: define a recurring need, bring the information into the team’s existing workflow, and give people a clearer starting point for decisions.

Claude is the agent in Matrixx’s implementation. Teams designing their own approach can consider Claude, Codex, Copilot, or their own agents, taking into account the capabilities and connection requirements of their chosen environment.

How do you make the workflow dependable?

A useful brief defines the accounts, publishers, reporting period, comparison, metrics, destination, and reviewer. For a Monday performance answer, it might read:

Every Monday, retrieve the previous week’s performance for the approved accounts and publishers, compare spend against the agreed pacing plan, identify exceptions, and prepare a summary for the account manager. Campaign changes are outside this workflow’s scope.

Then establish how the team will evaluate and manage it.

1. Define the data scope. Identify permitted accounts and publishers, reporting windows, currencies, time zones, and metric definitions.
2. Make the output inspectable. Ask the agent to identify its sources, highlight material gaps, and distinguish observed results from possible explanations.
3. Test against a known baseline. Compare the output with the team’s existing reporting over several cycles and record discrepancies.
4. Assign an owner and a failure path. Decide who reviews the output and what happens when access fails, information is incomplete, or the answer does not arrive.
5. Measure the whole workflow. Track preparation time, correction effort, recurring errors, and whether the team continues using the result.

These are requirements buyers should establish across their chosen agent, connected systems, and internal processes. Teams should verify how each requirement is supported before putting the workflow into production.

What controls belong around reporting?

Reporting workflows still handle sensitive information and can influence consequential decisions.

An agent might retrieve information from the wrong client account, deliver a recap to an unintended destination, or present an unsupported explanation with confidence. Sending a report into another system also introduces permissions and delivery requirements of its own.

Before deployment, teams should ask:

• Which accounts and information can the agent access?
• Where may it send the output?
• Who is responsible for checking its work?
• How will the team investigate an unexpected result?
• How can access be changed or removed?
• Who takes ownership when the workflow or its original owner changes?

Named ownership and clear escalation responsibilities are central to the accountability framework explored in “Who’s Accountable When AI Agents Buy Media?”

Starting with reporting gives teams a focused setting in which to resolve those questions.

How can teams progress from reporting to action?

As confidence grows, teams can define additional responsibilities and evaluate each one separately.

Luzern eCommerce provides an example of workflows extending across reporting, monitoring, and campaign updates. Its implementation uses approximately 125 scheduled runs for tracking, rank monitoring, and monthly reporting. Separately, reusable campaign-update workflows process approximately 1,900 updates per marketplace.

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That separation matters. Reporting and campaign updates involve different responsibilities, so teams should assess them against different requirements.

For a proposed change, the buyer’s checklist should cover who evaluates the recommendation, what evidence they need, and how approval is communicated. For execution, it should also cover permitted actions, spending boundaries, exception handling, and recovery procedures. Teams should confirm which requirements are supported by each system and which depend on their own operating processes.

An open-by-design approach such as Agent Connect supports agents built and run in the client’s own stack, connected to Skai’s cross-publisher infrastructure through MCP connectors. Teams can design around their preferred agent environment while drawing on Skai’s data, skills, tools, and retail media depth.

Skai’s Reporting, Insights, and Campaign Actions connectors are available now, with an Optimization connector coming soon. This gives teams a path from performance retrieval into campaign intelligence and eligible campaign actions, with the scope of each deployment determined by their requirements and the capabilities they have verified.

Each expansion deserves its own evaluation. Reliable reporting provides useful evidence about data retrieval and consistency; campaign execution adds decisions about authority, limits, and operational responsibility.

Choose a first job people can evaluate

Start with one of three jobs: answer a cross-publisher question, run recurring reporting, or propose a change for review.

Define the information needed, the expected output, the person responsible, and the authority the workflow requires. Use what the team learns to guide its next deployment.

Let your agent help identify and prepare the next move. Keep the right controls around what goes live.

Ready to put AI agents to work? Explore how Skai’s Agent Connect helps teams connect their preferred AI agents to cross-publisher data, insights, and campaign execution capabilities.

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