How to get data ready for AI agents

AI agents only perform as well as the context you give them. If you want to get data ready for AI agents in media, you have to feed them the file and the facts around it: metadata, rights, workflow state, and the feedback that says whether the last pass worked.

That is the lesson we keep running into at Flomenco, and it came up constantly at IBC in Amsterdam. An agent without context is a fast way to do the wrong work.

What does it mean to get data ready for AI agents?

Getting data ready for AI agents means putting the asset, its metadata, its rights, its workflow history, and its performance signal in one place the agent can use before it acts.

A folder of videos is not ready. A search index of those videos is closer, and still thin. Media work depends on relationships that live in other systems: an EIDR record, a rights window, a delivery spec, a previous QC note, last week’s audience numbers.

If those facts stay scattered, the agent fills the gaps. Guessing is expensive when the output is a master, a partner package, or a brand-facing clip.

This is why an agentic media platform has to hold context across the job, including the records around each file. Search can find the clip, and context still decides whether that clip is allowed to move.

Why do media agents stall on incomplete context?

The first wave of media AI was good at looking at the file: index the video, tag the frames, and find the sunset shot. That still matters, and it is the entry point for Discover.

The stall happens after the find. The agent can see the clip and still miss a wrong EIDR title, music cleared for social but not broadcast, a format last quarter’s reporting already killed, or a treatment a reviewer rejected.

We have been using AI on our own work at Flomenco, and the same pattern shows up. The model is rarely the bottleneck compared with the missing field.

For media, readiness includes the video plus the records that do not live in the file: identifiers from other metadata systems, rights, and the analytics that should steer the next cut.

Folder trees do not carry that context, and neither does a chat window that only saw the proxy.

What context should sit with every media agent?

Treat these as required inputs.

Context	What the agent needs	What goes wrong without it
Assets	The right version, related derivatives, and where the file lives	It works on a proxy, an old cut, or the wrong show
Metadata	EIDR and other schemas from the systems you already run	It mixes titles, episodes, or territories
Rights	Who can use what, where, and for how long	It proposes a use you cannot ship
Workflow	What already ran, who approved it, what is still open	It repeats a rejected path or skips a gate
Feedback	Analytics, partner rejects, and audience response	It optimizes for the last brief, not the last result

Assets. The agent has to know which version is current, which derivatives exist, and which storage system holds them. Flo connects to the libraries you already use, including S3, Box, and Google Drive, so the file does not have to be copied into a new silo before work can start.

Metadata. The useful identifiers often sit next to the video. EIDR is a common example: a title or cut only makes sense once it is tied to the record another metadata system already keeps. The job is to bring that schema into the workflow so the agent can use it.

Rights. A beautiful clip with no clearance is a hold. Rights context has to travel with the work so the agent can stop, flag, or choose a different source.

Workflow context. If review rejected a treatment yesterday, the next run should see that state. If delivery is waiting on legal, the agent should not publish around it.

External feedback. Analytics and audience response close the loop. If a cutdown got the clicks and another version did not, that result belongs in the next brief. Reporting should arrive in time to change the next cut, not after the campaign is over.

How does the Flo Media Agent Blueprint hold that context together?

The Flo Media Agent Blueprint is the name we use for how those inputs work as one loop. It is the operating pattern: connect the systems you already run, keep context attached as work moves, and send results back into the next pass.

That maps to Flo’s lifecycle:

1. Connect. Bring storage, media, metadata, and workflow systems into one operating layer.
2. Discover. Find the asset and the records around it.
3. Generate. Transform or prepare media with that context still attached.
4. Review. Keep humans on brand, rights, and publish calls.
5. Deliver. Move the approved output, then feed what happened back in.

The last step is the one teams skip. They index the library, run an agent, and stop. The Blueprint only works if delivery, reporting, and audience response become inputs again.

Flo sits beside the stack you already have. Keep the metadata system, the rights database, and the reporting tool. Stop making people copy facts between them so an agent can act.

What governance do agentic media workflows need?

Once agents can act, you need monitoring, governing, and observability: answers you can give on a Tuesday when something shipped wrong.

• Which agent ran, and on which version?
• What context did it see: asset, metadata, rights, prior review?
• Which model, rules, and checkpoints applied?
• Who approved the exception?
• What happened after delivery?

If you cannot answer those, the work is still a script with a friendly name.

Human-in-the-loop approval is part of that framework. AI can prepare the next step, and humans still own the call when the brand, the license, or the market is on the line. The workflow should record both.

Governance also means the agent is allowed to stop. Incomplete rights, a missing EIDR match, or a low-confidence QC flag should block delivery, not slide into a Slack thread after the file is gone.

How do you start getting your data ready?

You do not need a new archive to begin. You need a clear list of what an agent is allowed to use.

Inventory the sources. Write down where assets, metadata, rights, and analytics actually live today. Most teams already have the data. It is split across a bucket, a spreadsheet, a rights tool, and a dashboard.

Connect before you copy. Bring those systems into the workflow layer. Recreating every record in a new database is how projects stall.

Name the required fields. Decide which facts an agent must have before it can act. A find job may only need the asset and basic metadata. A publish job should also need rights and an approval state.

Put review on the irreversible steps. Brand, rights, and external publish stay gated. Deliver should move the approved version, with the decision still attached.

Feed the result back. Partner rejects, QC exceptions, and audience numbers belong on the next run. That is how the Blueprint stays a closed loop.

Frequently asked questions

What does it mean to get data ready for AI agents? It means the agent can see the asset and the operational facts around it (metadata, rights, workflow state, and feedback) before it acts.

Why is indexed video not enough? Indexing helps you find the file. Shipping the file still depends on identifiers, clearance, prior review, and whether the last version actually performed.

What is the Flo Media Agent Blueprint? It is the working name for Flo’s context loop: connect existing systems, keep assets and records together through discover, generate, review, and deliver, then return analytics and exceptions to the next run.

Do we have to replace our metadata or rights system? No. Those systems often hold the facts the agent needs. Flo is the layer that uses them in the workflow, beside the stack you already run.

How do we keep agents from acting on incomplete context? Define required fields per job, block delivery when they are missing, and keep human approval on rights, brand, and publish. Record what the agent saw and who signed off.