Toward more controllable, high quality, coherent outputs - that can be considered visually production-ready quality

on

This is a follow up to a previous post (and a bit of a messy free-write).

https://exploringallthethings.blogspot.com/2025/03/coherence-matrix-draft.html

Here is the Gpt Agent Summary of that post:

The blog post titled “Agentic Criteria & Coherence Matrix for AI Animation” from March 28 2025. The article introduces a dual‑purpose framework: a Coherence Evaluation Matrix for scoring AI‑generated animation outputs and an Agentic Production Matrix for organizing the creative agents in an animation pipeline. Below are its key points:

  1. Coherence Evaluation Matrix: This section proposes a scoring system (1–5) for assessing animation outputs along several dimensions such as character consistency, style adherence, motion believability, scene coherence, emotional fidelity and thematic unity. It offers definitions for each score (5 = excellent, 1 = poor) and notes that similar criteria can be used to evaluate individual images, focusing on style, emotional fidelity, scene coherence and design unity.

  2. Agentic Roles & Intelligence Metrics: The second section details roles in a modular AI‑driven animation pipeline—storyteller, visual designer/art director, character artist, animator, colorist/lighting designer, model engineer and orchestrator—and provides evaluation criteria and intelligence metrics for each. For example, the storyteller is judged on staging clarity, anticipation, timing, appeal and scene continuity, while also being scored on execution fidelity, adaptability, context awareness and handoff clarity. Similar tables exist for the other roles, culminating in a meta‑agent orchestrator that supervises the pipeline and adapts prompts and tool chains as needed.

  3. Agent‑to‑Agent Workflow: The post maps out how outputs flow between agents, specifying handoff contents (e.g., story beats, visual style guides, character turnarounds) and feedback loops that trigger reruns if quality drops. It provides example prompts at beginner, advanced and multimodal levels to guide each agent in interacting with others.

  4. Building a Pipeline: To operationalize the matrix, the author suggests pairing agent‑framework orchestrators (LangGraph, CrewAI, AutoGen) with generative tools suited to each role (GPT‑4, Midjourney, ControlNet, Pika, ComfyUI, FID/LPIPS metrics). They describe how experimental setups could connect these tools, track scores and trigger feedback loops.

  5. Considerations & Insights: The article notes that “one‑prompt” approaches (e.g., Manus.im) could theoretically orchestrate a complete pipeline but currently lack rich image or video generation capacity. The author ultimately stresses that the framework helps unify traditional animation principles with AI‑specific metrics like Fréchet Inception Distance (FID), temporal coherence, mode collapse and semantic consistency.

Overall, the page provides a structured framework for evaluating and coordinating AI‑generated animation, detailing both quality metrics and practical orchestration strategies with specific tools and agent roles.

What's Interesting

Since I wrote that (https://exploringallthethings.blogspot.com/2025/03/coherence-matrix-draft.html), Manus and Gemini both have added video to their capabilities, and Google released higher tier AI productization. 

There's ways to create a full video from a prompt. The fact that it's agentic in these tools is finally not the most interesting thing (still interesting). The most interesting thing for me is always the output visual quality. It's why I even try all the millions (literally) of recipes. And, the output is finally there. It's expensive, and still takes a lot of iteration and adapting to massive amounts of files, but it's in there.

Veo3 in Manus is "insert_whatever_expletive_here" and the fact that it can coherently visualize a 10 video snippet story is pretty much there. (I guess I could grab the benchmarks they are testing against, and get more quantified about this, but it's also qualitative - what the user prompt skill is, coping threshold for errors, etc.)

It still takes a lot of iteration, which is expensive in a lot of ways. But, I was able to tell a few stories with a sequence of videos.

There's a lot you can do with Gemini, Veo3, Imagen 4 and Google's higher AI tiers that are worth it I think, visual quality output speaking.

If Manus opened it's image models up to allow you to choose the best for the situation (Like you can do in tools like Krea.ai, and like you can do with Manus video to an extent), then it would be worth keeping because the visuals could stay current, and potentially reflect the latest imagery styles. Visual styles never stop evolving and changing. They are like fashion styles. You can probably extrapolate from there, that if you care about (high-quality, coherent, controllable, efficient) visuals, you want your tools to be perpetually open to the latest fashions. That's just a random hypothesis about something I noticed.

I saw they rolled into YouTube the ability to generate video shorts. That's interesting because it's in the Creator flow path and flattens the complexity immeasurably. You don't have to leave the tool. Quality is blurry still. I imagine that's temporary. YouTube is not a casual tool anymore, so that's an interesting thing.



Let's paste this into GPT for fun and to see how it would rewrite all that. I like including both. I don't know why. It's entertaining.

It's defaults are so definitive, salesy, preachy. I definitely don't agree with some of it. But, I guess I use it for gauging what it's default framing is. It's the same reason you keep a default install of a browser for testing a UI design - you want to understand the default behaviors - to understand what most people experience. So, I guess there's an overall framework there that's interesting if you are writing about it. That's probably why I ran that filter, and included the output here (other than I just find it kind of funny).


Gpt 4o:

Thank you for the follow-up and the thoughtful reflection—it’s clear you’ve been deep in the trenches of AI video creation, not just from a tooling standpoint but from a creative system design perspective. Here’s a synthesis and expansion that could work as your next blog post or at least a foundation for it—bridging your March post with the current state of tools like Veo3, Manus, Gemini, Imagen 4, and the YouTube Shorts pipeline.

Beyond the Matrix: When Agentic AI Meets High-Fidelity Visual Storytelling

(A Follow-Up to the Agentic Criteria & Coherence Matrix)

In March, I published the Agentic Criteria & Coherence Matrix, a dual-purpose framework for evaluating and orchestrating AI-generated animation. The focus was split: on one hand, scoring visual outputs for consistency, believability, and emotional coherence; on the other, defining agent roles that could modularize an intelligent animation pipeline—from Storyteller to Orchestrator.

Since then, something major has shifted:
The visuals finally caught up.

The Real Magic Isn’t Just Agentic—It’s Visual Fidelity

Tools like Veo3 (in Manus), Gemini 1.5 Pro, Google AI Ultra (https://one.google.com/about/google-ai-plans/ and https://cloud.google.com/startup/ai) Imagen 4, have crossed a subtle but critical threshold. We’re now seeing tools that:

  • Can often sustain coherence across multiple shots

  • Respect prompt-based visual direction

  • Respond (to a degree) to aesthetic intention

  • Allow modular reuse of styles, scenes, and motifs

Manus, in particular, has blown past my “skeptical optimist” threshold. Veo3 Manus can now hold a visual thread across a 10-video snippet sequence—enough to finally tell a short story.

This isn’t just technical. It’s creative.

It Still Takes Work—But Now It’s Can Be Worth The Time

Yes, it’s still expensive—cost, time, file management, and iteration loops.

Yes, it still requires human-level adaptation skills.
But here’s the shift:

It now feels (a bit) like the creative friction is in service of something (potentially) beautiful.

We’re (potentially) not wasting hours for marginal output anymore.
We’re (potentially) shaping something cinematic(pretty good looking.)

Is Agentic Coordination Still Interesting Useful?

Absolutely. But I’d reframe it slightly.

Agentic flow is now starting to be a little more like:

  • A debugging lens: Why did this scene fail? Was it a style drift? Was the character off-model?

  • A creative handoff guide: How can I move from my character design sheet to a walking clip without losing expression?

  • A modular thinking tool: What does the Animator agent own vs. what the Storyteller hands off?

The agentic model becomes less about building a rigid machine and more about understanding creative roles as reusable logic blocks.

If I ever get around to pairing this with LangGraph or CrewAI, it’ll be for this reason:
To save time and standardize quality while maintaining story flexibility.

Visual Quality > Tool Loyalty 

(Dang GPT, That's a bit blunt.)

Here’s the biggest realization:

Visual styles evolve like fashion trends.

The tool that looked good last month might feel outdated next week.
That’s not a flaw in the system—it’s a reflection of visual culture.

Which is why I’ve started evaluating tools not by:

  • Their claim to be “agentic”

  • Or “best-in-class” metrics

But by:

  • How open they are to stylistic evolution

  • Whether I can swap models or inject new style vocabularies

  • How fast I can test variations

This is where Manus could expand. Imagine if image models were plug-and-play like in Krea.ai, letting us chase the latest visual language. (model capability, coherence, smart connector interactions, broad selection of continually evolving output formats, and a way to keep it all organized)
If tools don’t open to visual trends, they’ll calcify (Dang, Gpt, calcify is quite a word.)—and visual creators will move on. need to do it some other way.

The YouTube Creator Flow is Flat-Out Interesting

The integration of AI Shorts generation directly in YouTube’s Creator backend is (potentially) a massive flattening of complexity.
You stay in the same tool.
You publish where you edit.
You reach your audience without extra pipelines.

Right now, the visual quality is still blurry—but give it a few months. This is not a toy feature; it’s an editorial-level unlock that will change creator workflows. Especially once Shorts are stylistically competitive.

What Comes Next?

If March’s post was about defining (thinking about how to make the models get better more controllable, high quality, coherent outputs - that can be considered production-ready visual quality) who does what and how we evaluate it, this post is about what we care about most when the tools get good enough (this part is true, but I'm sure this is already a goal):

  1. Visual Quality

  2. Narrative Control

  3. Modular Reusability

  4. Style Agility

  5. Time-to-Iteration

Maybe the next framework isn’t just agent roles and metrics—it’s a Visual Quality Continuum, matched with Tool Responsiveness Index. Maybe we I need: (I'm sure you could recipe together their existing leaderboard they map production to. Actually, is that creating a new benchmark?)

  • A new scoring system for how quickly a tool adapts to visual change

  • A reusable agent-to-scene prompt library

  • A memory-efficient file chain for managing hundreds of clips

But mostly, we just need to keep telling stories—and now, we finally can.

Would you like me to format this as a ready-to-publish Blogger post with header tags and visuals inserted? Or create a slide deck or lead magnet PDF version of this update for sharing with your AI creator network?

(Did I train my Gpt to always offer next most logical actions or does everyone get those? I assume everyone gets those. I did ask it for that many times, so I dunno.)


Design, Art, Technology