Defining Technical Delivery Specs

To use AI for routine production, you must shift from being a tool-user to an agent-designer. As a tool-user, you interact with visual interfaces—clicking a drop-down menu in Adobe Photoshop or selecting 'Export for Web' from a list of options. While this works for one file, it is imprecise for an AI agent that needs to handle hundreds of assets without drifting from your intent.

An AI agent—a proactive program that follows a goal—operates best with a written manual or a command-line style set of instructions. Instead of showing the agent a menu, you provide a bounded manual: a document that explicitly defines the input, the process, and the output. For example, instead of saying "Export this for the web," you write: "Convert source PSD to JPG, set color profile to sRGB, dimensions to 2000x1000px, and quality to 80%."

By replacing visual clicks with explicit written rules, you remove the agent's ability to guess. You are no longer just operating software; you are designing the logic that the software follows to ensure professional control over every single export.

To prevent an AI agent from making assumptions, you must provide exact delivery specifications. Ambiguity is the primary cause of production errors; a term like "high resolution" means different things to a web developer and a print house. To maintain professional control, your manual must define three non-negotiable pillars for every asset: the color profile, the dimensions, and the file format.

Consider a web-shop hero image. If you tell an agent to "make it for the web," it might choose a PNG that is too large or a CMYK profile that looks shifted in a browser. Instead, you define a precise technical requirement: "sRGB color profile, 2000x1000 pixels, and JPG format at 80% quality."

By specifying sRGB, you ensure color consistency across screens. By setting the pixels to 2000x1000, you guarantee the layout doesn't break. By choosing JPG at 80%, you balance visual clarity with page load speed. You will apply this level of detail to every asset in your set, turning a vague creative request into a binary checklist that the agent can either verify as "passed" or flag as "failed."

When you deliver a campaign, you rarely ship one size. You ship a set of variants—different crops and formats for different platforms. An agent cannot hold these variables in its head; it needs a structured specification matrix. This is typically an XLSX file that maps every asset ID to its specific delivery rules.

For example, imagine your project requires a vertical crop for TikTok and a horizontal thumbnail for YouTube. In your matrix, the TikTok row would specify a 9:16 aspect ratio (1080x1920px) and a .MP4 or .JPG format. The YouTube row would specify a 16:9 aspect ratio (1280x720px) and a .JPG format.

By organizing your requirements this way, the agent doesn't have to 'think' about the platform. It simply looks up the Asset ID in the matrix, finds the corresponding rule for dimensions and format, and compares those numbers against the actual export. If the matrix says 1080x1920 and the file is 1080x1080, the agent marks it as 'failed' immediately. You use this matrix as the single source of truth that governs every automated check in the production chain.

One of the biggest risks in automated production is "hallucinated" sources. If an AI agent is told to export a file but cannot find the correct source, its default tendency is to find the closest match and proceed. In a professional workflow, a 'close match' is a failure. If the agent uses a low-resolution JPG because the .AI vector file is missing, you may not notice until the asset is printed and looks pixelated.

To prevent this, you must program a strict rule against guessing. Instruct your agent to pause the workflow the moment a required input is missing. For example, if an agent is processing a request for a logo export but finds no .AI vector file attached to the asset ID, it must not look for a .PNG alternative. Instead, the agent sends one clear notification to the producer: "Missing source .AI for Asset ID 104; process paused."

By forcing the agent to stop and follow up exactly once, you ensure that no asset is ever built on a guessed reference. You maintain a clean chain of custody from the original source to the final delivery, keeping the human producer as the only person authorized to provide the missing file.

Technical validity—having the right dimensions and color profile—does not equal legal validity. In a high-volume production environment, it is tempting to let an agent mark everything that 'looks right' as passed. However, if an agent treats an unknown permission status as an implicit approval, you risk shipping assets that the client has not cleared or that violate copyright. This is a critical failure in the chain of custody.

To prevent this, you must define a 'hard stop' condition for permissions. A stop condition is a rule that tells the agent to freeze the process immediately, regardless of whether the file is technically perfect. For example, imagine your asset register has a permission field with three possible statuses: 'Approved', 'Pending', and 'Unknown'. If the agent encounters a source image marked as 'Unknown', it must block the export. Even if the image is the perfect 1080x1080 size and in the correct sRGB profile, the 'Unknown' status triggers the stop condition. The agent flags the asset for human review and refuses to add it to the delivery packet. By treating silence as a 'no', you ensure that every shipped asset has an explicit human sign-off.

A bounded manual is only effective if its logic follows a strict sequence. If you tell an agent to verify dimensions before checking permissions, you waste computing power on a file that may never be legally allowed to ship. To maximize efficiency and safety, you must organize your instructions into a logical hierarchy that prioritizes stop conditions over technical checks. The most critical filters—identity and legality—must always come first.

Consider the correct order for an export check: first, the agent must Identify the Asset ID to ensure it is looking at the right project. Second, it must Check Permissions; if the status is 'Unknown', the process stops here. Only after the asset is identified and cleared does the agent move to technical verification. Third, it will Verify Dimensions against the specification matrix. Finally, it will Confirm the Color Profile to ensure visual consistency.

By structuring your manual in this order, you create a series of gates. A file must pass through the identity and permission gates before it ever reaches the technical check. You will use this sequence to build every agent manual in your production chain, ensuring that no time is spent polishing an asset that should have been blocked at the start.

The final step in designing your agent is validation. A manual is only 'bounded' if it leaves zero room for the agent to make creative decisions. In professional production, an agent that 'tries its best' is a liability. If your instructions contain subjective language—such as 'optimize,' 'fit,' or 'make it look right'—you have accidentally given the agent permission to guess. This is where technical checks can bypass visual intent, leading to crops that lose the product or profiles that shift color.

To validate your manual, review every line for subjective adjectives and replace them with binary values. For example, an instruction that says "Make sure the image fits social media" is a failure; the agent might crop the image to a square, but it might cut off the top of the product to do so. Instead, you must write: "Verify dimensions are exactly 1080x1080 pixels." The first instruction asks for a creative judgment; the second asks for a mathematical match. By auditing your manual for these 'leaks,' you ensure the agent remains a technician and you remain the creative director.