Agentic Criteria & Coherence Matrix
This dual-purpose framework includes both: (1) a Coherence Evaluation Matrix for analyzing AI-generated animation output, and (2) an Agentic Production Matrix for designing, evaluating, and orchestrating intelligent creative agents in a modular animation pipeline.
Section 1: Coherence Evaluation Matrix (Output-Focused)
This section scores the quality of the animation based on traditional and AI-adapted artistic criteria.
Top-Level Coherence Snapshot
| Element | Description | Score (1–5) |
|---|---|---|
| Character Consistency | Facial/pose/geometry continuity across frames | |
| Style Adherence | Color, line, and shape language matching reference style | |
| Motion Believability | Natural motion transitions, speed, and weight | |
| Scene Coherence | Logical scene transitions and object persistence | |
| Emotional Fidelity | Alignment of tone with narrative intent (e.g., joy, wonder) | |
| Thematic Unity | Symbolic and narrative cohesion across the sequence |
Scoring Key (1–5)
| Score | Meaning |
|---|---|
| 5 | Excellent – Fully aligned, high-quality, and coherent output |
| 4 | Good – Minor inconsistencies but solid performance overall |
| 3 | Adequate – Meets basic requirements; room for improvement |
| 2 | Needs Improvement – Gaps in logic, quality, or alignment |
| 1 | Poor – Output is incoherent, off-target, or unusable |
Standalone Image Evaluation (Still Frames & Visuals)
Although this matrix is optimized for sequential animation workflows, it can also be applied to still images. Evaluating single AI-generated visuals—like keyframes, illustrations, or concept art—benefits from the same criteria:
- Style Adherence: Does the image align with a defined visual language or reference?
- Emotional Fidelity: Is the mood or feeling consistent with narrative intent?
- Scene Coherence: Are all elements logically integrated (lighting, shadows, proportions)?
- Design Unity: Do characters, props, and background elements feel part of the same world?
This makes the matrix useful for scoring outputs from Midjourney, DALL·E, Stable Diffusion, and other image models, especially in contexts like storytelling, branding, product design, or previsualization.
Section 2: Agentic Production Matrix (Agent-Focused)
This section defines the roles, sequence, intelligence metrics, and interactions of each agent in a modular, recursive, AI-driven animation workflow.
Orchestrator (Meta-Agent)
- Function: Supervises pipeline, adapts prompts, reroutes agents, runs scoring logic
- Behavior: Recursive, dynamic, feedback-responsive
- Position: Not a step in the linear sequence—functions as the system conductor
Linear Agent Sequence
| Agent | Focus | Sequence |
|---|---|---|
| Storyteller | narrative structure | 1 |
| Visual Designer | style and tone | 2 |
| Character Artist | form and identity | 3 |
| Colorist / Lighting | emotion and visibility | 4 |
| Animator | motion and timing | 5 |
| Model Engineer | coherence, fidelity, ML integration | 6 |
Agent Role: Storyteller
| Principle / Metric | Evaluation Criteria | Score |
|---|---|---|
| Staging | Clarity of narrative focus in each scene | |
| Anticipation | Use of visual cues to foreshadow events | |
| Timing | Emotional pacing that matches story beats | |
| Appeal | Characters and visuals that support the story’s tone | |
| Scene Continuity | Logical progression and visual consistency between scenes | |
| Mood Progression | Emotional tone that evolves meaningfully over time |
Agentic Intelligence Metrics: Storyteller
| Metric | Description | Score |
|---|---|---|
| Execution Fidelity | Performs the expected role tasks reliably and accurately | |
| Adaptability | Responds appropriately to changing goals, prompts, or conditions | |
| Context Awareness | Understands or infers context from prior or surrounding content | |
| Tool Interoperability | Can use, combine, or delegate to tools as needed | |
| Handoff Clarity | Produces structured, usable output for next agents in the chain | |
| Self-Evaluation Capability | Can reflect, rerun, or evaluate its own outputs with scoring logic |
Agent Role: Visual Designer / Art Director
| Principle | Evaluation Criteria | Score |
|---|---|---|
| Color | Palette choices enhance emotion and hierarchy | |
| Shape Language | Consistent stylization across characters and environments | |
| Texture / Style | Unified visual style across the sequence | |
| Lighting | Creates atmosphere and directs attention | |
| Exaggeration | Visual distortion to enhance clarity or emotion |
Agentic Intelligence Metrics: Visual Designer / Art Director
| Metric | Description | Score |
|---|---|---|
| Execution Fidelity | Performs the expected role tasks reliably and accurately | |
| Adaptability | Responds appropriately to changing goals, prompts, or conditions | |
| Context Awareness | Understands or infers context from prior or surrounding content | |
| Tool Interoperability | Can use, combine, or delegate to tools as needed | |
| Handoff Clarity | Produces structured, usable output for next agents in the chain | |
| Self-Evaluation Capability | Can reflect, rerun, or evaluate its own outputs with scoring logic |
Agent Role: Character Artist
| Principle / Metric | Evaluation Criteria | Score |
|---|---|---|
| Solid Drawing | Characters maintain form, perspective, and structure across poses and frames | |
| Character Consistency | Facial features, proportions, and outfits remain on-model and recognizable | |
| Secondary Action | Subtle actions (like blinking, breathing, or gestures) support the primary action | |
| Design Coherence | Visual identity (props, costume, silhouette) is preserved throughout the sequence |
Agentic Intelligence Metrics: Character Artist
| Metric | Description | Score |
|---|---|---|
| Execution Fidelity | Performs the expected role tasks reliably and accurately | |
| Adaptability | Responds appropriately to changing goals, prompts, or conditions | |
| Context Awareness | Understands or infers context from prior or surrounding content | |
| Tool Interoperability | Can use, combine, or delegate to tools as needed | |
| Handoff Clarity | Produces structured, usable output for next agents in the chain | |
| Self-Evaluation Capability | Can reflect, rerun, or evaluate its own outputs with scoring logic |
Agent Role: Colorist / Lighting Designer
| Principle / Metric | Evaluation Criteria | Score |
|---|---|---|
| Mood Conveyance | Color palette and lighting effectively communicate emotional tone | |
| Scene Contrast | Good use of value and color contrast to direct viewer focus | |
| Harmony | Color relationships are aesthetically pleasing and unified across the sequence | |
| Color Grading | Scenes shift in tone using color to reflect emotional or story changes | |
| Style Transfer / Bias | Avoids unintended color artifacts caused by AI hallucination or style blending |
Agentic Intelligence Metrics: Colorist / Lighting Designer
| Metric | Description | Score |
|---|---|---|
| Execution Fidelity | Performs the expected role tasks reliably and accurately | |
| Adaptability | Responds appropriately to changing goals, prompts, or conditions | |
| Context Awareness | Understands or infers context from prior or surrounding content | |
| Tool Interoperability | Can use, combine, or delegate to tools as needed | |
| Handoff Clarity | Produces structured, usable output for next agents in the chain | |
| Self-Evaluation Capability | Can reflect, rerun, or evaluate its own outputs with scoring logic |
Agent Role: Animator
| Principle / Metric | Evaluation Criteria | Score |
|---|---|---|
| Squash and Stretch | Provides volume and elasticity to characters during motion | |
| Follow Through | Secondary motion elements continue naturally after primary action | |
| Arc | Motion follows natural, curved paths | |
| Slow In / Slow Out | Motion eases in and out for realism | |
| Pose-to-Pose | Strong key poses with fluid interpolation between frames | |
| Gesture Dynamics | Expressive body language and facial performance |
Agentic Intelligence Metrics: Animator
| Metric | Description | Score |
|---|---|---|
| Execution Fidelity | Performs the expected role tasks reliably and accurately | |
| Adaptability | Responds appropriately to changing goals, prompts, or conditions | |
| Context Awareness | Understands or infers context from prior or surrounding content | |
| Tool Interoperability | Can use, combine, or delegate to tools as needed | |
| Handoff Clarity | Produces structured, usable output for next agents in the chain | |
| Self-Evaluation Capability | Can reflect, rerun, or evaluate its own outputs with scoring logic |
Agent Role: Model Engineer / ML Evaluator
| ML Metric | Evaluation Focus | Score |
|---|---|---|
| FID (Fidelity) | How visually close the output is to training data or target style | |
| Temporal Coherence | Does the animation avoid flickering or warping between frames? | |
| Controllability | How reliably the model responds to prompts or conditions | |
| Semantic Consistency | Character identities and scene logic are preserved throughout | |
| Diversity | Output variety across multiple generations |
Agentic Intelligence Metrics: Model Engineer / ML Evaluator
| Metric | Description | Score |
|---|---|---|
| Execution Fidelity | Performs the expected role tasks reliably and accurately | |
| Adaptability | Responds appropriately to changing goals, prompts, or conditions | |
| Context Awareness | Understands or infers context from prior or surrounding content | |
| Tool Interoperability | Can use, combine, or delegate to tools as needed | |
| Handoff Clarity | Produces structured, usable output for next agents in the chain | |
| Self-Evaluation Capability | Can reflect, rerun, or evaluate its own outputs with scoring logic |
Agent Role: Orchestrator (Meta-Agent)
| System Function | Evaluation Focus | Score |
|---|---|---|
| Tool Chaining | Ability to chain tools (e.g., Krea → Pika → Runway) to complete full animation pipeline | |
| Prompt Adaptation | Dynamically adjusts prompts or inputs mid-process to optimize outcomes | |
| Style Matching | Selects appropriate models, LoRAs, or visual filters for the project’s tone | |
| Iteration Strategy | Automatically detects low scores and triggers reprocessing or regeneration steps | |
| Scene Planning | Determines logical sequence flow and defines per-scene goals (style, motion, tone) | |
| Memory and Reusability | Remembers effective model chains and setups for future reuse |
Agentic Intelligence Metrics: Orchestrator (Meta-Agent)
| Metric | Description | Score |
|---|---|---|
| Execution Fidelity | Performs the expected role tasks reliably and accurately | |
| Adaptability | Responds appropriately to changing goals, prompts, or conditions | |
| Context Awareness | Understands or infers context from prior or surrounding content | |
| Tool Interoperability | Can use, combine, or delegate to tools as needed | |
| Handoff Clarity | Produces structured, usable output for next agents in the chain | |
| Self-Evaluation Capability | Can reflect, rerun, or evaluate its own outputs with scoring logic |
Agent-to-Agent Workflow Flow
| From Agent | To Agent | Handoff Contents | Purpose | Feedback Loop |
|---|---|---|---|---|
| Storyteller | Visual Designer | Story beats, mood, tone, symbolism | Set visual direction based on narrative | If theme misalignment occurs |
| Visual Designer | Character Artist | Style guide, shape language, silhouette specs | Align characters with visual identity | If design coherence is low |
| Character Artist | Animator | Turnarounds, gestures, rigs | Enable motion with consistent anatomy | If pose breaks model form |
| Animator | Colorist / Lighting | Timing sheets, arcs, scene focus | Enhance emotion and clarity with light/color | If visual tension is unclear |
| Animator | Model Engineer | Output frames, motion logs | Detect artifacts, coherence issues | If warping/jitter occurs |
| Model Engineer | Orchestrator | Performance metrics, tool feedback | Decide stack changes or reruns | If scores fall below threshold |
| Orchestrator | Any Agent | Reroute, regenerate, tune instructions | Manage refinement strategy | Loop triggered by score drop |
Section 3: Agent-to-Agent Workflow Flow
This table defines how outputs move between agents and where feedback loops exist.
| From Agent | To Agent | Handoff Contents | Purpose | Feedback Loop |
|---|---|---|---|---|
| Storyteller | Visual Designer | Story beats, tone, symbolism | Establish mood and direction | If misaligned tone is detected |
| Visual Designer | Character Artist | Style guide, shape rules | Ensure on-model design | If visual identity diverges |
| Character Artist | Animator | Turnarounds, rigs, gestures | Support animation fidelity | If pose breaks consistency |
| Animator | Colorist / Lighting | Motion rhythm, scene arcs | Apply emotional nuance | If emotion clarity fails |
| Animator | Model Engineer | Rendered frames, timing logs | Detect flicker, artifacts | If frame-level issues arise |
| Model Engineer | Orchestrator | Metrics, fidelity scores | Trigger reruns or changes | If thresholds aren't met |
Example Prompts by Agent
Storyteller (Agent 1):
Generate a 3-act story outline with emotionally resonant beats. Include scene-level descriptions, key character arcs, and symbolic themes. Format the output for downstream use by the Visual Designer.
Generate a 3-act story outline with emotionally resonant beats. Include scene-level descriptions, key character arcs, and symbolic themes. Format the output for downstream use by the Visual Designer.
Visual Designer / Art Director (Agent 2):
Based on the narrative from the Storyteller, create a visual direction document including: color palette, shape language, lighting intent, and texture style. Ensure the tone aligns with the emotional arc of the story.
Based on the narrative from the Storyteller, create a visual direction document including: color palette, shape language, lighting intent, and texture style. Ensure the tone aligns with the emotional arc of the story.
Character Artist (Agent 3):
Design character sheets for all primary and secondary characters using the Visual Designer’s guide. Include turnarounds, key expressions, and consistent outfit elements across scenes.
Design character sheets for all primary and secondary characters using the Visual Designer’s guide. Include turnarounds, key expressions, and consistent outfit elements across scenes.
Colorist / Lighting Designer (Agent 4):
Apply color grading and lighting decisions to support the emotional tone of each scene. Reference the Animator’s motion focus and the Visual Designer’s style guide. Ensure visual contrast and mood clarity.
Apply color grading and lighting decisions to support the emotional tone of each scene. Reference the Animator’s motion focus and the Visual Designer’s style guide. Ensure visual contrast and mood clarity.
Animator (Agent 5):
Create keyframe sequences based on the Character Artist’s assets and story pacing. Emphasize expressive gesture dynamics, arc-based motion, and squash/stretch when appropriate. Return interpolated frame ranges and timing sheets.
Create keyframe sequences based on the Character Artist’s assets and story pacing. Emphasize expressive gesture dynamics, arc-based motion, and squash/stretch when appropriate. Return interpolated frame ranges and timing sheets.
Model Engineer / ML Evaluator (Agent 6):
Evaluate the output animation sequence for temporal coherence, fidelity, and semantic consistency. Compare the current frame series with reference keyframes provided by the Animator. Use FID to assess style adherence and flag any frame-level artifacts such as warping, jitter, or character drift. If coherence metrics fall below threshold, suggest specific model tuning steps or reroute to the Orchestrator for regeneration.
Evaluate the output animation sequence for temporal coherence, fidelity, and semantic consistency. Compare the current frame series with reference keyframes provided by the Animator. Use FID to assess style adherence and flag any frame-level artifacts such as warping, jitter, or character drift. If coherence metrics fall below threshold, suggest specific model tuning steps or reroute to the Orchestrator for regeneration.
Orchestrator (Meta-Agent):
Monitor outputs from all agents in sequence. If any agent score falls below 3, trigger the appropriate rerun with adjusted parameters. Optimize the prompt chain and maintain memory of successful stacks for reuse. Dynamically reassign or sequence tools as needed.
Monitor outputs from all agents in sequence. If any agent score falls below 3, trigger the appropriate rerun with adjusted parameters. Optimize the prompt chain and maintain memory of successful stacks for reuse. Dynamically reassign or sequence tools as needed.
Additional Example Prompts by Agent and Level
The Beginner, Advanced, and Multimodal prompts, within the agent-to-agent structure, map to user roles and system sophistication:
Beginner
For users or systems with simple prompting needs.
Works well for UI-level agents or early-stage human guidance.
Pairs with single-modality text generation or simple tool tasks.
Advanced
Designed for expert users or more autonomous agents.
These prompts include nuanced creative or evaluative intent.
Useful for agents that do complex reasoning or use style logic, e.g., Storyteller or Animator.
Multimodal
Adds context-rich data across text, image, motion, or metric-based inputs.
Ideal for AI systems that combine modalities (e.g., vision + language).
Supports more realistic orchestration of toolchains or pipelines (Krea, Pika, etc.).
Prompt Levels:
Beginner: Simple, accessible instructions for novice users or basic agent behavior.
Advanced: Detailed prompts with nuanced creative direction, evaluation, or logic chaining.
Multimodal: Combines visual, motion, and text inputs or outputs to simulate real production complexity.
Beginner: Simple, accessible instructions for novice users or basic agent behavior.
Advanced: Detailed prompts with nuanced creative direction, evaluation, or logic chaining.
Multimodal: Combines visual, motion, and text inputs or outputs to simulate real production complexity.
Storyteller → Visual Designer:
Beginner: Generate a children's story outline with 3 scenes, each with distinct mood and setting.
Advanced: Create a symbolic story arc using visual metaphors and emotional pacing. Provide per-scene descriptors for tone and design intent.
Multimodal: Output a text-based story outline with matching visual cue prompts (e.g. "a glowing forest filled with lavender mist").
Beginner: Generate a children's story outline with 3 scenes, each with distinct mood and setting.
Advanced: Create a symbolic story arc using visual metaphors and emotional pacing. Provide per-scene descriptors for tone and design intent.
Multimodal: Output a text-based story outline with matching visual cue prompts (e.g. "a glowing forest filled with lavender mist").
Visual Designer → Character Artist:
Beginner: Create a visual style guide with 3 shapes, 2 lighting moods, and 5 colors.
Advanced: Design a style sheet including color palette, lighting intent, and shape grammar aligned with story themes.
Multimodal: Return reference images or prompts for each visual element (e.g. "triangular forms + golden hour lighting").
Beginner: Create a visual style guide with 3 shapes, 2 lighting moods, and 5 colors.
Advanced: Design a style sheet including color palette, lighting intent, and shape grammar aligned with story themes.
Multimodal: Return reference images or prompts for each visual element (e.g. "triangular forms + golden hour lighting").
Character Artist → Animator:
Beginner: Draw a character turnaround and 3 basic poses for animation setup.
Advanced: Generate a consistent character sheet with full rotation, emotional expressions, and gesture intent.
Multimodal: Provide both sketch and text prompts for use in generative animation pipelines.
Beginner: Draw a character turnaround and 3 basic poses for animation setup.
Advanced: Generate a consistent character sheet with full rotation, emotional expressions, and gesture intent.
Multimodal: Provide both sketch and text prompts for use in generative animation pipelines.
Animator → Colorist / Lighting:
Beginner: Animate a simple greeting with 3 poses and suggest matching lighting (e.g. morning, warm tones).
Advanced: Produce an arc-based movement sequence with scene-level lighting notes for emotion support.
Multimodal: Return keyframes and timing sheets annotated with color/lighting cues per frame group.
Beginner: Animate a simple greeting with 3 poses and suggest matching lighting (e.g. morning, warm tones).
Advanced: Produce an arc-based movement sequence with scene-level lighting notes for emotion support.
Multimodal: Return keyframes and timing sheets annotated with color/lighting cues per frame group.
Colorist / Animator → Model Engineer:
Beginner: Compare color tone across 2 scenes and check for consistency.
Advanced: Detect frame-level inconsistencies using pixel metrics and emotional scoring trends.
Multimodal: Use embeddings, color histograms, and gesture logs to test animation coherence.
Beginner: Compare color tone across 2 scenes and check for consistency.
Advanced: Detect frame-level inconsistencies using pixel metrics and emotional scoring trends.
Multimodal: Use embeddings, color histograms, and gesture logs to test animation coherence.
Model Engineer → Orchestrator:
Beginner: Summarize which parts of the animation feel off and why.
Advanced: Evaluate the full pipeline, identify bottlenecks, score each agent's output, and propose rerouting logic.
Multimodal: Aggregate metrics from animation, lighting, and style prompts into a feedback matrix for next run.
Beginner: Summarize which parts of the animation feel off and why.
Advanced: Evaluate the full pipeline, identify bottlenecks, score each agent's output, and propose rerouting logic.
Multimodal: Aggregate metrics from animation, lighting, and style prompts into a feedback matrix for next run.
Orchestrator → All Agents:
Beginner: Ask agents to try again when scores are low.
Advanced: Route new instructions to specific agents, chain new tools, or update model parameters.
Multimodal: Merge narrative, motion, and visual coherence scores to optimize the full loop and reuse strong passes.
Beginner: Ask agents to try again when scores are low.
Advanced: Route new instructions to specific agents, chain new tools, or update model parameters.
Multimodal: Merge narrative, motion, and visual coherence scores to optimize the full loop and reuse strong passes.
Building an Agentic AI Pipeline
This section explains how the Agentic Criteria & Coherence Matrix could hypothetically be realized using current AI agent frameworks, creative tools, and orchestration logic.
Agent Frameworks / Orchestrators
- LangGraph: DAG-based orchestration with memory and feedback loops
- CrewAI: Role-based agents simulating creative collaboration
- AutoGen (Microsoft): Multi-agent chat-based orchestration
- OpenInterpreter: Local LLM-based tool runner with scripting
Creative + Generative Tools (Per Agent Role)
| Agent Role | Tools |
|---|---|
| Storyteller | GPT-4, Claude 3, Mistral (narrative generation) |
| Visual Designer | Midjourney, DALL·E 3, Krea, Kandinsky 3 |
| Character Artist | ControlNet, Leonardo.Ai, Draw Things |
| Animator | Pika, Runway, AnimateDiff, Deforum |
| Colorist / Lighting | ComfyUI, LUTs, video editing tools |
| Model Engineer | FID/LPIPS tools, Hugging Face metrics |
| Orchestrator | LangGraph, CrewAI, Python scripts |
Experimental Setup Example
To prototype a full agentic loop, combine these:
- Use LangGraph to define agent flow with feedback
- Connect foundation models (OpenAI, Anthropic, Hugging Face)
- Route outputs through visual tools (Krea → Pika → Runway)
- Track scores using metrics like FID, motion coherence, or prompt quality
- Display feedback in a simple web app (e.g. Streamlit dashboard)
This approach could allow partial or full simulation of the Agentic Matrix using today’s tools — adaptable over time as AI systems mature.
Valuable Insight: One-Prompt Agentic Pipeline (Manus.im)
With tools like Manus.im, it's now hypothetically possible to simulate a creative AI pipeline within a single structured prompt. This would streamline agent orchestration and evaluation using memory, turn-taking, and feedback rerouting.
Example Prompt Idea for Full Pipeline Simulation
You are an orchestrator managing a multi-agent AI animation pipeline using the Agentic Criteria & Coherence Matrix. Simulate a sequential creative process across these roles:
1. Storyteller – narrative, tone, symbolism
2. Visual Designer – style, shape language, lighting
3. Character Artist – consistent characters, expressions
4. Animator – motion dynamics, timing, gesture
5. Colorist / Lighting – emotional color and contrast
6. Model Engineer – fidelity, coherence, reroute logic
Each agent:
- Outputs structured content
- Scores its output (1–5)
- Passes handoff notes
- Reruns if score < 3
Begin with the Storyteller. End with an evaluation summary.
This can be tested on Manus.im or adapted for orchestration frameworks like LangGraph, CrewAI, or AutoGen.
Core References & Concepts
This section highlights key terms and foundational ideas that both inform the matrix and help readers understand how agentic animation could be evaluated.
- Agent: A role-based module in the pipeline that performs specific creative or evaluative tasks within an orchestrated system.
- Agentic AI: AI systems that demonstrate intentional, goal-directed behavior.
- CUA (Computer Using Agent): An intelligent agent capable of using software tools autonomously to complete tasks.
- FID (Fréchet Inception Distance): Measures similarity between generated and real images; used to evaluate style fidelity and visual coherence in AI-generated animation.
- Temporal Coherence: Stability of motion and elements across video frames.
- Mode Collapse: When a generative model produces low-diversity or repetitive outputs.
- Semantic Consistency: Maintaining meaning and identity across visual or textual outputs.
- Controllability: A model's ability to respond accurately to prompts or inputs.
- 12 Principles of Animation: Core traditional animation principles established by Disney.
Metadata
- Author: S. Martin Jagiello
- Version: 1.0 (March 28, 2025)
- License: All rights reserved.
- Website: stephaniejagiello.com