2026 is the year AI agents start doing operational work, and feed management is no exception. We’ve built the AI Feed Agent that can interpret your feed configuration, answer questions about rules and mappings, and help you diagnose and resolve issues faster.
Why We Built an AI Feed Agent for Feed Management?
Managing product feeds at scale is not just about exports. It is about understanding how data flows from source to mapping to channel, and being able to react quickly when something breaks.
Most teams lose time switching between feeds, rules, Merchant Center diagnostics, and documentation. The goal of the Feedoptimise AI Feed Agent is to reduce that friction by giving you a single interface where you can ask questions, run diagnostics, and perform actions.
In the video, we walk through exactly how that works.
Asking Questions About Your Feeds
A basic expectation from an agent is that it understands the platform and the current setup. Instead of opening multiple feeds, you can ask the agent to find specific rules across the system.
You can see how the agent locates a rule inside a Google Shopping UK feed, explains the logic behind existing custom labels, and summarises how values are calculated. It can also show who changed a rule and when.
The agent is not limited to explaining what already exists. It can suggest new rules, like stock-based segmentation, and guide you step by step through setting them up inside Feedoptimise.
Diagnosing Feed and Channel Issues
The agent can run a full feed audit and check whether the feed complies with Google product feed specifications. It highlights missing attributes, value distribution for fields like availability or gender, and quality indicators such as title length.
From the audit summary, you can drill into detailed results for each analysed field and see exactly which items are affected.
The diagnostics go beyond Feedoptimise itself. In the video, we connect a test Google Merchant Center account and pull real disapprovals. The agent groups issues by cause, explains why they are happening, and suggests what needs to change to fix them.
You can also diagnose individual products. In the example shown, a product with three variants is analysed, and all errors and warnings are pulled for each variant.
For issues like mismatched prices, the agent can even check website structured data to confirm what Google’s crawler is actually seeing.
Performing Actions Through the Agent
The final section of the video focuses on actions. The agent can help with simple operational tasks like blocking products from a feed. In the example, the most expensive dress in a Shopify store is blocked after a quick confirmation.
It can also handle configuration tasks, such as setting import schedules. Instead of configuring this manually, the schedule is set through a prompt.
After changes are made, the agent can force an immediate source or feed update so you do not have to wait for the next scheduled run.
Get Access to the Feedoptimise AI Feed Agent
The video shows the agent in action using real feeds and real scenarios. To explore it yourself, you can sign up for a free trial and get hands-on access to the Feedoptimise AI Feed Agent.
This lets you ask questions about your own feeds, run diagnostics, and try agent-assisted actions directly inside the platform.
What does the future hold?
This is just the beginning. We started with a highly practical assistant that can walk you through setups, answer diagnostic questions, run audits, update feeds and schedules, and block items when needed.
Next, we are expanding it so it can also create new feeds end to end, including channel templates, mappings, and initial rules, then safely edit and maintain those rules over time (not just explain them).
We are also working on report analysis, so it can interpret feed and channel diagnostics over time, highlight trends and recurring issues, and recommend the highest-impact fixes based on what is changing in your catalog.
On top of that, we are building support for overrides and supplemental feeds, so you can apply targeted attribute fixes or channel-specific adjustments without touching the source data. The agent will be able to create and connect these supplemental layers to the right primary feeds, keep the relationships clear, and validate the combined output before it is sent to each channel.
Longer term, we want it to support full migrations, for example moving from another feed tool or rebuilding a complex setup across multiple channels, with checks, validation, and a clear change log so teams can review what changed and why.
Stay tuned for the latest AI tools and features for feed management and optimisation.