AI Agent Tomi: Automating Retail Media Campaign Operations

Retail media has become one of the fastest-growing channels in digital advertising, but managing campaigns across multiple retailers can quickly turn into a tedious, manual grind. Topsort’s new AI agent, Tomi, is designed to take over much of that operational burden. Instead of spending hours adjusting bids, pacing budgets, and monitoring performance, marketers can rely on Tomi to handle repetitive tasks while they focus on strategy. This article explores what an AI agent like Tomi can realistically automate, how it fits into existing retail media workflows, and what brands and marketplaces should consider before adopting it.

What Is Tomi and Why It Matters for Retail Media

Retail media has exploded as brands shift more budgets to on-site search, sponsored listings, display, and off-site extensions powered by retailers’ shopper data. With this growth has come a surge in operational complexity: more campaigns, more SKUs, more bid strategies, and more reporting lines to manage. Topsort’s launch of Tomi, an AI agent for automating retail media campaign operations, is a response to this growing strain on marketing and marketplace teams.

While the public announcement headline focuses on Tomi as an AI agent that automates retail media operations, the underlying idea is broader: turn repetitive, rule-based ad operations into a software-driven workflow, while leaving human marketers in charge of goals, strategy, and oversight. In practice, this means Tomi is positioned as a smart assistant living inside a retail media platform, continuously monitoring campaigns and making changes within defined guardrails.

The Rise of Retail Media — and Operational Overload

Before understanding what an AI agent like Tomi actually does, it helps to look at the environment it is entering. Retail media is no longer just a test budget on a single marketplace; it is a core performance channel for many brands and agencies.

Why Retail Media Is So Demanding

Retail media operations are challenging for several reasons:

• SKU-level complexity: Brands can easily advertise hundreds or thousands of products across a single retailer, each with unique margins, seasonality, and stock constraints.
• Multi-retailer fragmentation: Large advertisers manage campaigns across numerous retailers, each using different platforms, taxonomies, and reporting standards.
• Granular bidding controls: Sponsored products and search ads often require keyword or query-level bidding, increasing the volume of tweaks and tests.
• Continuous monitoring: Performance can shift daily due to competition, price changes, and inventory swings, requiring rapid adjustments.

This environment creates a heavy burden on ad operations teams, who spend much of their day exporting reports, adjusting bids, pausing underperforming placements, and rebalancing budgets. It is this repetitive, high-frequency optimization work that AI agents like Tomi attempt to automate.

What an AI Agent Like Tomi Actually Does

Although the official announcement keeps things high level, the role of an AI agent in retail media is reasonably clear from industry best practices. Tomi is likely designed to sit between strategic goals (for example, “improve return on ad spend” or “maximize share of shelf for a new launch”) and the low-level operational actions required to get there.

Core Responsibilities of a Retail Media AI Agent

An AI agent for retail media campaign operations typically covers several core functions:

• Budget and pacing automation: Adjusting daily spend, reallocating budgets between campaigns or ad groups, and ensuring budgets last through the day or promotional period.
• Bid optimization: Increasing or decreasing bids based on performance data, placement competition, and inventory availability.
• Campaign hygiene: Pausing low-performing keywords or products, activating winners more aggressively, and consolidating overlapping segments.
• Alerting and anomaly detection: Flagging sudden shifts in performance, out-of-stock situations, or tracking issues for human review.
• Experimentation support: Automating structured tests (such as bid multipliers or creative variants) within safe bounds set by the advertiser.

In other words, Tomi’s job is to convert high-level objectives into a stream of tactical changes that would otherwise require a human operator to be inside the platform all day.

How Tomi Fits into a Retail Media Stack

Topsort is known in the industry as a technology provider that enables marketplace-style advertising and auction infrastructure for retailers and platforms. Tomi can be viewed as an added intelligence layer on top of that infrastructure, aimed at brands, agencies, and marketplace operators who want to streamline the “last mile” of day-to-day optimization.

Roles Across the Ecosystem

Different stakeholders can benefit from an AI agent in different ways:

• Brands and advertisers: Reduce the effort required to manage campaigns across multiple marketplaces, while keeping control over goals and critical constraints such as margins and inventory.
• Retailers and marketplaces: Offer a more sophisticated, easy-to-use ad experience to advertisers, improving adoption and spend without requiring each brand to staff a large operations team.
• Agencies: Free specialists from manual bid changes so they can focus on strategy, creative, and cross-channel planning.

Instead of replacing an entire team, Tomi acts as a co-pilot or optimizer that scales existing teams’ capabilities.

Key Benefits of Automating Retail Media Operations

Moving from manual operations to AI-assisted workflows can unlock several tangible benefits. While every deployment is different, organizations adopting tools like Tomi can reasonably expect improvements in speed, consistency, and strategic focus.

1. Time Savings and Scale

Most ad operations teams hit a natural ceiling: there are only so many campaigns, keywords, and products a person can adjust in a day. Automation breaks this limit by acting continuously and programmatically.

• Campaigns receive more frequent and granular optimizations.
• Teams avoid “set and forget” configurations that drift away from current conditions.
• New products or campaigns can be onboarded faster, since templates and rules can be reused.

2. More Consistent Optimization Decisions

Human judgment is crucial for strategy, but it can be inconsistent at scale. AI agents apply the same logic to every optimization decision, leading to smoother performance curves and fewer oversights.

• Rules and models are applied uniformly across markets and portfolios.
• Bias from short-term fluctuations or anecdotal experience is reduced.
• Complex constraints (for example, minimum ROAS by product group) can be enforced systematically.

3. Faster Reaction to Market Changes

Retail environments move quickly: competitors change prices, products go out of stock, and seasonal spikes appear with little warning. AI agents can monitor data at higher frequency than human teams.

• Bids can be adjusted within hours or even minutes as conditions shift.
• Spending can be redirected from out-of-stock products to available alternatives.
• Promotional windows can be capitalized on with clear rules and pacing targets.

4. Strategic Reallocation of Human Effort

The central promise of tools like Tomi is not simply cost savings, but a better allocation of human creativity and expertise. When teams no longer spend most of their day on repetitive updates, they can invest more effort in:

• Developing retail-specific creative and landing experiences.
• Working with sales and merchandising teams to align media with trade objectives.
• Designing robust testing roadmaps and long-term growth plans.

What Tasks Should Stay Human-Driven?

Even as an AI agent takes on more operational work, there are critical areas where human marketers remain central. Understanding these boundaries helps brands deploy Tomi sensibly and avoid over-automation.

Strategic Decisions and Guardrails

Human teams should continue to own key strategic decisions, such as:

• Defining business objectives (awareness, profitability, category share, sell-through).
• Setting acceptable ranges for metrics like ROAS, cost per acquisition, and share of voice.
• Prioritizing product lines, campaigns, or territories.
• Determining when to push aggressive growth versus maintain efficiency.

Tomi can operate inside these boundaries, but it should not define them.

Brand, Compliance, and Customer Understanding

AI agents excel at quantitative decisions, but human oversight is especially important in areas that involve nuance and long-term brand impact:

• Ensuring creative assets and ad placements reflect brand positioning.
• Respecting legal, compliance, and category-specific rules.
• Interpreting shopper research, qualitative feedback, and retailer relationships.

In this sense, Tomi is a powerful execution engine that must be steered by people who understand the broader brand and customer context.

Comparing Manual, Rule-Based, and AI-Agent Approaches

Retail media teams often progress through a journey: from fully manual optimization, to rules-based automation, and finally to AI-driven agents. Understanding the differences helps clarify where Tomi fits.

Implementing an AI Agent: Practical Steps for Teams

For organizations considering Tomi or a similar AI agent, a phased, intentional rollout will typically bring better results than switching everything on at once. Below is a structured approach to adoption.

Step-by-Step Rollout Plan

1. Clarify objectives and constraints: Decide what success looks like. Are you optimizing for revenue, profitability, inventory clearance, or category share? Document non-negotiable constraints, such as target margins or compliance rules.
2. Start with a pilot scope: Choose a manageable subset of campaigns, products, or retailers where you can test Tomi’s impact without risking core revenue streams.
3. Define guardrails and permissions: Set minimum and maximum bids, budget ranges, and pacing rules. Decide which actions Tomi can take autonomously and where approvals are required.
4. Monitor and compare performance: Run the pilot long enough to capture normal variability. Compare against historical performance or a control group run manually or with rules-based automation.
5. Refine settings and trust levels: Adjust goals, constraints, and thresholds based on learnings. As confidence grows, allow Tomi to manage more levers or a broader portfolio.
6. Scale across brands and retailers: Once the model and workflow are validated, roll out to additional brands, product lines, or marketplaces, adapting for local nuances where needed.
7. Institutionalize new workflows: Update operating procedures, training, and reporting so that AI-assisted operations are fully integrated into day-to-day work.

Data, Measurement, and Transparency Considerations

AI-based automation is only as effective as the data and feedback loops that power it. Before deploying Tomi, teams should think carefully about what data the agent can access and how its decisions will be measured and explained.

Essential Data Inputs

Retail media AI agents typically rely on several core data types:

• Performance metrics: Impressions, clicks, conversions, sales, revenue, and derived KPIs like ROAS or cost per click.
• Product data: Price, margins or cost estimates, inventory levels, categories, and attributes.
• Campaign metadata: Structures, targeting rules, negative keywords, and promotional flags.
• External context (where available): Seasonal periods, promotional calendars, and competitor activity signals.

Ensuring that these inputs are timely and accurate greatly increases the value an agent like Tomi can deliver.

Transparency and Explainability

Marketers and retailers are more likely to trust an AI agent when they can understand what it is doing and why. Topsort’s implementation details are not fully described in the brief announcement, but in general, effective AI-enabled retail media tools should provide:

• Change logs: A clear record of how bids, budgets, or statuses were altered and when.
• Rationale summaries: Human-readable explanations of major changes (for example, “bid reduced due to sustained low ROAS”).
• Control toggles: The ability to override specific decisions or revert to prior configurations when needed.

Risk Management, Governance, and Team Skills

Adopting an AI agent for retail media is not just a technical shift; it is also an operational and governance shift. Teams need to adapt roles, responsibilities, and oversight processes.

Managing Risks and Safeguards

To avoid unintended outcomes, organizations should establish a few key protections:

• Spending caps and thresholds: Limit maximum daily or campaign-level spend managed by the agent, especially in early stages.
• Exception alerts: Configure alerts for unusual behaviors, such as extreme bid changes or sudden drops in performance.
• Review cadence: Run regular (for example, weekly) performance reviews focused specifically on AI-driven optimizations and learnings.

Evolving Roles and Skills

When Tomi takes on more of the tactical execution, roles in the marketing and marketplace teams typically shift:

• From execution to orchestration: Specialists manage the configuration and supervision of AI agents rather than making every individual change themselves.
• From narrow channel focus to cross-channel strategy: Time saved in retail media can be reinvested in integrating learnings with search, social, and CRM efforts.
• From ad operations to experimentation: Teams design better tests and measurement frameworks to get maximum value from automation.

Training becomes essential: everyone using Tomi needs to understand both its capabilities and its limits.

What Topsort’s Launch of Tomi Signals About the Market

The decision by Topsort to introduce an AI agent dedicated to retail media campaign operations reflects a broader trend across digital advertising: platforms are moving from tool providers to intelligent co-pilots. Instead of simply offering interfaces and APIs, they are embedding logic that learns from data and takes action on behalf of users.

For retailers, this trend can make their media networks more attractive to brands, particularly those who lack large in-house teams. For brands and agencies, it suggests that mastering collaboration with AI agents will become a core operational skill, similar to how bidding strategies and attribution modeling became essential in earlier eras of digital marketing.

While Tomi is one specific implementation from a particular vendor, it represents a class of tools that are likely to become standard in retail media over the next few years. Organizations that experiment early, with clear guardrails and learning goals, will be better positioned as automation becomes the norm rather than the exception.

Final Thoughts

Topsort’s launch of Tomi, an AI agent to automate retail media campaign operations, is a sign that the industry is moving decisively beyond manual and rules-only optimization. By offloading repetitive tasks like bid adjustments, budget pacing, and campaign hygiene, Tomi promises to give marketers more time and headspace for strategy, experimentation, and cross-channel planning.

To realize that promise, however, brands, agencies, and retailers must treat AI agents not as black boxes, but as powerful collaborators. That means defining clear objectives and constraints, starting with well-structured pilots, investing in data quality and transparency, and evolving team skills so that humans remain firmly in charge of direction while software handles the heavy lifting. If approached thoughtfully, AI agents like Tomi can turn the operational complexity of retail media into a competitive advantage rather than a burden.

Editorial note: This article is an independent analysis based on publicly available information about the launch of Tomi as an AI agent for retail media operations. For the original announcement, see the source here.