Creative Agents

Agents for creative tasks: writing, design, image generation, video, music, and content creation.

Creative Agent Capabilities

Creative Agent Architectures

1. Writing Agent

class WritingAgent:
    def __init__(self):
        self.styles = StyleLibrary()
        self.templates = TemplateLibrary()
        self.editor = EditorTools()

    async def write(self, spec: WritingSpec) -> WrittenPiece:
        # 1. Select style/voice
        style = self.styles.get(spec.style) if spec.style else self.analyze_voice(spec.samples)

        # 2. Research if needed
        research = await self.research(spec.topic) if spec.needs_research else None

        # 3. Outline
        outline = await self.outline(spec, research)

        # 4. Draft
        draft = await self.draft(outline, style, spec)

        # 5. Edit & polish
        polished = await self.edit(draft, style, spec.constraints)

        # 6. Format
        formatted = await self.format(polished, spec.format)

        return WrittenPiece(
            content=formatted,
            word_count=len(formatted.split()),
            readability=self.analyze_readability(formatted),
            seo_score=self.analyze_seo(formatted, spec.keywords)
        )

    async def edit(self, text: str, style: Style, constraints: Constraints) -> str:
        # Grammar & spelling
        corrected = await self.grammar_check(text)

        # Style consistency
        styled = await self.apply_style(corrected, style)

        # Constraints
        constrained = self.apply_constraints(styled, constraints)

        # Flow & clarity
        polished = await self.improve_flow(constrained)

        return polished

2. Copywriting Agent

class CopywritingAgent:
    def __init__(self):
        self.frameworks = CopyFrameworks()
        self.ab_tester = ABTester()

    async def write_copy(self, brief: CopyBrief) -> CopyVariants:
        # Generate multiple variants using different frameworks
        variants = await asyncio.gather(*[
            self.write_with_framework(framework, brief)
            for framework in self.frameworks.get_for_goal(brief.goal)
        ])

        # Score variants
        scored = await self.score_variants(variants, brief)

        return CopyVariants(variants=scored)

    async def write_with_framework(self, framework: CopyFramework, brief: CopyBrief) -> CopyVariant:
        if framework == "AIDA":
            return await self.write_aida(brief)
        elif framework == "PAS":
            return await self.write_pas(brief)
        elif framework == "BAB":
            return await self.write_bab(brief)
        elif framework == "FAB":
            return await self.write_fab(brief)
        elif framework == "QUEST":
            return await self.write_quest(brief)

3. Content Strategy Agent

class ContentStrategyAgent:
    def __init__(self):
        self.calendar = ContentCalendar()
        self.seo = SEOAnalyzer()
        self.analytics = ContentAnalytics()

    async def plan_content(self, strategy: ContentStrategy) -> ContentPlan:
        # 1. Audit existing content
        audit = await self.audit_existing(strategy.channels)

        # 2. Keyword research
        keywords = await self.seo.research(strategy.topics, strategy.competitors)

        # 3. Gap analysis
        gaps = self.identify_gaps(audit, keywords)

        # 4. Generate ideas
        ideas = await self.generate_ideas(gaps, strategy.pillars)

        # 5. Prioritize
        prioritized = self.prioritize(ideas, strategy.goals)

        # 6. Schedule
        calendar = self.calendar.schedule(prioritized, strategy.frequency)

        return ContentPlan(
            calendar=calendar,
            keywords=keywords,
            pillars=strategy.pillars,
            kpis=strategy.kpis
        )

Visual Creative Agents

1. Image Generation Agent

class ImageGenerationAgent:
    def __init__(self):
        self.models = {
            "midjourney": MidjourneyClient(),
            "dalle3": DALL_E3Client(),
            "stable_diffusion": SDClient(),
            "flux": FluxClient(),
            "ideogram": IdeogramClient()
        }
        self.upscaler = Upscaler()
        self.editor = ImageEditor()

    async def generate(self, spec: ImageSpec) -> ImageResult:
        # Select best model for task
        model = self.select_model(spec)

        # Build prompt
        prompt = self.build_prompt(spec)

        # Generate
        images = await self.models[model].generate(
            prompt=prompt,
            aspect_ratio=spec.aspect_ratio,
            style=spec.style,
            quality=spec.quality,
            seed=spec.seed
        )

        # Post-process
        if spec.upscale:
            images = await asyncio.gather(*[
                self.upscaler.upscale(img, factor=spec.upscale_factor)
                for img in images
            ])

        if spec.edit_instructions:
            images = await asyncio.gather(*[
                self.editor.edit(img, spec.edit_instructions)
                for img in images
            ])

        return ImageResult(images=images, prompt=prompt, model=model)

    def select_model(self, spec: ImageSpec) -> str:
        if spec.style == "photorealistic":
            return "midjourney" if spec.quality == "high" else "flux"
        elif spec.style == "illustration":
            return "dalle3"
        elif spec.style == "logo":
            return "ideogram"
        elif spec.style == "text_in_image":
            return "ideogram"
        return "stable_diffusion"  # Most flexible

2. Video Generation Agent

class VideoGenerationAgent:
    def __init__(self):
        self.models = {
            "sora": SoraClient(),
            "runway": RunwayGen3Client(),
            "pika": PikaClient(),
            "luma": LumaDreamMachineClient(),
            "kling": KlingClient(),
            "veo": VeoClient()
        }
        self.editor = VideoEditor()

    async def generate(self, spec: VideoSpec) -> VideoResult:
        model = self.select_model(spec)

        # Generate
        if spec.type == "text_to_video":
            video = await self.models[model].text_to_video(
                prompt=spec.prompt,
                duration=spec.duration,
                aspect_ratio=spec.aspect_ratio,
                fps=spec.fps
            )
        elif spec.type == "image_to_video":
            video = await self.models[model].image_to_video(
                image=spec.image,
                prompt=spec.prompt,
                duration=spec.duration
            )
        elif spec.type == "video_to_video":
            video = await self.models[model].video_to_video(
                video=spec.source_video,
                prompt=spec.prompt
            )

        # Post-process
        if spec.edit_instructions:
            video = await self.editor.edit(video, spec.edit_instructions)

        if spec.add_audio:
            video = await self.add_audio(video, spec.audio_spec)

        return VideoResult(video=video, metadata=self.extract_metadata(video))

3. Design Agent

class DesignAgent:
    def __init__(self):
        self.figma = FigmaClient()
        self.canva = CanvaClient()
        self.adobe = AdobeExpressClient()

    async def create_design(self, spec: DesignSpec) -> DesignResult:
        # 1. Research & moodboard
        moodboard = await self.create_moodboard(spec)

        # 2. Generate concepts
        concepts = await self.generate_concepts(spec, moodboard)

        # 3. Refine selected
        refined = await self.refine_concept(concepts[0], spec)

        # 4. Create deliverables
        deliverables = await self.create_deliverables(refined, spec.deliverables)

        # 5. Design system
        if spec.design_system:
            system = await self.create_design_system(refined)

        return DesignResult(
            concept=refined,
            deliverables=deliverables,
            design_system=system
        )

4. 3D/AR/VR Agent

class ThreeDAgent:
    def __init__(self):
        self.models = {
            "meshy": MeshyClient(),
            "tripo": TripoClient(),
            "spline": SplineClient(),
            "bezi": BeziClient()
        }
        self.optimizer = ModelOptimizer()

    async def generate_3d(self, spec: ThreeDSpec) -> ThreeDResult:
        model = self.select_model(spec)

        if spec.type == "text_to_3d":
            model_output = await self.models[model].text_to_3d(
                prompt=spec.prompt,
                style=spec.style,
                polygon_count=spec.polygon_count
            )
        elif spec.type == "image_to_3d":
            model_output = await self.models[model].image_to_3d(
                image=spec.image,
                views=spec.views
            )
        elif spec.type == "video_to_3d":
            model_output = await self.models[model].video_to_3d(
                video=spec.video
            )

        # Optimize
        optimized = await self.optimizer.optimize(
            model_output,
            target_polygons=spec.target_polygons,
            format=spec.format
        )

        # Convert formats
        formats = await self.convert_formats(optimized, spec.formats)

        return ThreeDResult(models=formats, metadata=model_output.metadata)

5. Music/Audio Agent

class MusicAgent:
    def __init__(self):
        self.models = {
            "suno": SunoClient(),
            "udio": UdioClient(),
            "stable_audio": StableAudioClient(),
            "musiclm": MusicLMClient(),
            "audioldm": AudioLDMClient()
        }
        self.editor = AudioEditor()
        self.mastering = MasteringEngine()

    async def generate_music(self, spec: MusicSpec) -> MusicResult:
        model = self.select_model(spec)

        if spec.type == "text_to_music":
            audio = await self.models[model].text_to_music(
                prompt=spec.prompt,
                duration=spec.duration,
                style=spec.style,
                tempo=spec.tempo,
                key=spec.key
            )
        elif spec.type == "continuation":
            audio = await self.models[model].continue_audio(
                audio=spec.seed_audio,
                duration=spec.duration
            )
        elif spec.type == "stem_separation":
            stems = await self.models[model].separate_stems(spec.audio)
            return MusicResult(stems=stems)

        # Mastering
        if spec.master:
            audio = await self.mastering.master(audio, spec.target_loudness)

        return MusicResult(audio=audio, metadata=self.extract_metadata(audio))

    async def generate_sound_effect(self, spec: SFXSpec) -> AudioResult:
        return await self.models["audioldm"].text_to_audio(
            prompt=spec.prompt,
            duration=spec.duration
        )

Creative Workflows

1. Campaign Creation Workflow

graph TD
    A[Brief] --> B[Research & Strategy]
    B --> C[Concept Development]
    C --> D[Copywriting]
    C --> E[Visual Design]
    D --> F[Copy Review]
    E --> G[Design Review]
    F --> H[Integration]
    G --> H
    H --> I[Stakeholder Review]
    I --> J[Revisions]
    J --> K[Final Approval]
    K --> L[Production]
    L --> M[Distribution]

2. Brand Identity Workflow

graph TD
    A[Brand Brief] --> B[Research & Audit]
    B --> C[Strategy & Positioning]
    C --> D[Visual Identity]
    D --> E[Logo Design]
    D --> F[Color Palette]
    D --> G[Typography]
    D --> H[Iconography]
    E --> I[Design System]
    F --> I
    G --> I
    H --> I
    I --> J[Guidelines Document]
    J --> K[Asset Production]
    K --> L[Implementation]

3. Social Media Content Pipeline

graph LR
    A[Content Calendar] --> B[Idea Generation]
    B --> C[Copywriting]
    C --> D[Visual Creation]
    D --> E[Video Production]
    C --> F[Copy Review]
    D --> G[Design Review]
    E --> G
    F --> H[Scheduling]
    G --> H
    H --> I[Publishing]
    I --> J[Engagement Monitoring]
    J --> K[Performance Analysis]
    K --> A

Creative Tools & Platforms

Image Generation

Video Generation

Music/Audio

Design

3D Generation

Evaluation & Quality

Creative Quality Metrics

Quality Control

class CreativeQualityControl:
    def __init__(self):
        self.aesthetic = AestheticScorer()
        self.brand = BrandComplianceChecker()
        self.accessibility = AccessibilityChecker()
        self.plagiarism = PlagiarismDetector()

    async def validate(self, creative: CreativeOutput) -> QCResult:
        checks = await asyncio.gather(
            self.aesthetic.score(creative),
            self.brand.check(creative),
            self.accessibility.check(creative),
            self.plagiarism.check(creative)
        )

        return QCResult(
            passed=all(c.passed for c in checks),
            scores={c.name: c.score for c in checks},
            issues=[i for c in checks for i in c.issues]
        )

Legal & Ethics

Copyright & IP

class IPCompliance:
    def __init__(self):
        self.copyright = CopyrightChecker()
        self.trademark = TrademarkChecker()
        self.licensing = LicenseValidator()

    async def check(self, creative: CreativeOutput) -> IPReport:
        return IPReport(
            copyright=self.copyright.check(creative),
            trademark=self.trademark.check(creative),
            licensing=self.licensing.validate(creative.assets)
        )

Bias & Fairness

class BiasAuditor:
    def __init__(self):
        self.demographic = DemographicAnalyzer()
        self.stereotype = StereotypeDetector()

    async def audit(self, creative: CreativeOutput) -> BiasReport:
        return BiasReport(
            demographic=self.demographic.analyze(creative),
            stereotypes=self.stereotype.detect(creative),
            recommendations=self.generate_recommendations(creative)
        )

Related Notes

• Function Calling — Creative tools as functions
• MCP Protocol — Creative tools via MCP
• Browser Automation — Web research for creative
• Code Execution — Creative coding
• API Integration — Creative platform APIs
• Custom Tools — Building creative tools
• OpenAI Agents SDK — Creative tools in SDK
• LangGraph — Creative workflows
• PydanticAI — Structured creative output
• Multimodal Foundations — Multimodal models
• Small Language Models — SLMs for creative
• Coding Agents — Creative coding
• Customer Facing Agents — Marketing agents
• Agent Benchmarks — Creative benchmarks
• Observability — Creative observability
• Cost Optimization — Creative cost optimization
• Secure Coding — Secure creative code
• Creative Agents — Frontier creative AI
• Research Directions — Creative AI research

Sources: Midjourney, DALL-E, Stable Diffusion, Flux, Ideogram, Recraft, Sora, Runway, Pika, Luma, Kling, Veo, Suno, Udio, Stable Audio, MusicLM, AudioLDM, Figma, Canva, Adobe Express, Spline, Bezi, Meshy, Tripo, CSM, Luma Genie, Spline, Runway, Pika, Luma, Kling, Sora, Midjourney, DALL-E, Stable Diffusion, Flux, ControlNet, LoRA, DreamBooth, Textual Inversion, CLIP, BLIP, LAION, Aesthetic Predictor, LAION-Aesthetics, PickScore, ImageReward, HPS, FID, CLIP Score, IS, FVD, FVMD, Inception Score, Precision/Recall, SSIM, PSNR, LPIPS, Brand Guidelines, WCAG, Section 508, Copyright Law, Fair Use, DMCA, Creative Commons, AI Art Copyright, Training Data Ethics, Bias in Generative AI, Stereotype Detection, Demographic Parity, Equalized Odds, Counterfactual Fairness.