Advanced & Creative

Multi-agent architectures and creative AI applications pushing the frontier of what agents can do.

Advanced & Creative Agents

These agents go beyond single-task automation. They represent the frontier of agentic AI: multi-agent collaboration, creative co-creation, and emergent problem-solving.

🛠 Active Use Cases

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🤖 Multi-Agent Collaborative Storytelling

NoteDescription

A system of specialized agents that collaborate to produce rich, coherent long-form narratives. A Planner Agent sets the story arc and characters. A Writer Agent drafts scene by scene. A Critic Agent reviews for consistency and engagement. A Continuity Agent maintains a world-state memory to prevent contradictions. This architecture produces quality that exceeds a single LLM call.

TipImplementation — Python

from anthropic import Anthropic

client = Anthropic()

def planner_agent(premise: str) -> str:
    response = client.messages.create(
        model="claude-opus-4-6",
        max_tokens=1024,
        system="You are a story architect. Create tight story structures.",
        messages=[{"role": "user", "content": f"Create a 3-act structure and 3 main characters for: {premise}"}]
    )
    return response.content[0].text

def writer_agent(story_plan: str, scene_number: int, world_state: str) -> str:
    response = client.messages.create(
        model="claude-opus-4-6",
        max_tokens=1500,
        system="You are a literary author. Write vivid, emotionally resonant scenes.",
        messages=[{"role": "user", "content": f"Plan:\n{story_plan}\n\nWorld state:\n{world_state}\n\nWrite scene {scene_number}."}]
    )
    return response.content[0].text

def critic_agent(scene: str) -> str:
    response = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=512,
        system="You are a literary editor. Be specific and actionable.",
        messages=[{"role": "user", "content": f"Review this scene for pacing, emotion, and show-don't-tell:\n\n{scene}\n\nProvide 3 specific improvements."}]
    )
    return response.content[0].text

def update_world_state(world_state: str, new_scene: str) -> str:
    response = client.messages.create(
        model="claude-haiku-4-5-20251001",
        max_tokens=256,
        system="Extract and update story facts concisely.",
        messages=[{"role": "user", "content": f"Current state:\n{world_state}\n\nNew scene:\n{new_scene}\n\nUpdate the world state."}]
    )
    return response.content[0].text

# Orchestration
premise = "A retired astronaut discovers her late husband's AI still runs on an abandoned space station."
plan = planner_agent(premise)
world_state = "Characters established. Station: ISS-Omega. Year: 2041."

for scene_num in range(1, 4):
    draft = writer_agent(plan, scene_num, world_state)
    feedback = critic_agent(draft)
    world_state = update_world_state(world_state, draft)
    print(f"--- Scene {scene_num} ---\n{draft}\n\nEditor notes:\n{feedback}\n")

WarningImplementation — Low/NoCode

Stack: n8n + Claude API (multiple calls) + Notion + Airtable (world state)

1. Setup: An Airtable base stores the world state (character facts, locations, plot events).
2. Planner: First Claude API call generates the story plan and character sheets, saved to Notion.
3. Writer Loop: For each scene, n8n: fetches world state from Airtable → calls Claude to write → calls Claude again to review.
4. State Update: After each approved scene, Airtable records are updated with new story facts.
5. Assembly: Once all scenes are written, n8n concatenates them into a final Notion document.

---

🎨 AI Art Direction & Prompt Engineering Agent

NoteDescription

An agent that transforms a high-level creative brief (brand, mood, target audience, campaign objective) into a suite of optimized image generation prompts for Midjourney, DALL-E, or Stable Diffusion. It handles style consistency across a campaign, generates variants for A/B testing, and iterates based on feedback — acting as an AI art director.

TipImplementation — Python

from anthropic import Anthropic

client = Anthropic()

def generate_prompts(brief: dict) -> str:
    response = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=2000,
        system="""You are a world-class art director and prompt engineer.
Generate precise, detailed image prompts for AI image generators.
Each prompt should specify: subject, style, lighting, color palette, mood, technical parameters (aspect ratio, quality modifiers).""",
        messages=[{
            "role": "user",
            "content": f"""Creative brief:
Brand: {brief['brand']}
Campaign: {brief['campaign']}
Mood: {brief['mood']}
Target audience: {brief['audience']}
Platforms: {brief['platforms']}

Generate 5 diverse prompts (hero image, 2 variants, mobile crop, social square).
Format each for Midjourney (include --ar, --style, --v 6.1 parameters)."""
        }]
    )
    return response.content[0].text

def iterate_on_feedback(original_prompt: str, feedback: str) -> str:
    response = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=512,
        messages=[{
            "role": "user",
            "content": f"Original prompt: {original_prompt}\n\nFeedback: {feedback}\n\nRewrite the prompt incorporating this feedback."
        }]
    )
    return response.content[0].text

brief = {
    "brand": "EcoFlow — premium sustainable outdoor gear",
    "campaign": "Spring 2026 collection launch",
    "mood": "adventurous, clean, hopeful, connected to nature",
    "audience": "25-40 outdoor enthusiasts, eco-conscious",
    "platforms": "Instagram, Pinterest, Web hero banner"
}
prompts = generate_prompts(brief)
print(prompts)

WarningImplementation — Low/NoCode

Stack: Airtable + Make + Claude API + DALL-E API + Canva API

1. Brief Input: Creative team fills an Airtable form with campaign brief fields.
2. Prompt Generation: Make triggers, sends the brief to Claude API, which returns 5 structured prompts.
3. Image Generation: Each prompt is sent to the DALL-E 3 API.
4. Review Board: Generated images + prompts are organized in an Airtable gallery view for art director review.
5. Iteration: Rejected images trigger a Make sub-scenario that sends feedback to Claude for prompt revision.

---

🚀 Product Ideation & Validation Agent

NoteDescription

A multi-perspective agent that evaluates product ideas from four angles simultaneously: Market Opportunity, Technical Feasibility, Business Model Viability, and User Desirability. It synthesizes these into a structured investment memo, accelerating early-stage product decisions.

TipImplementation — Python

from anthropic import Anthropic
import concurrent.futures

client = Anthropic()

ANALYSTS = {
    "market": "You are a venture capitalist specializing in market analysis. Assess TAM, competition, and market timing with specific data points.",
    "technical": "You are a CTO with 20 years experience. Assess technical feasibility, build timeline, and key technical risks.",
    "business": "You are a CFO and business model expert. Assess monetization options, unit economics, and path to profitability.",
    "user": "You are a UX researcher specializing in Jobs-to-be-Done. Assess pain point intensity, user segments, and willingness to pay.",
}

def analyze_from_perspective(idea: str, role: str, system_prompt: str) -> tuple:
    response = client.messages.create(
        model="claude-opus-4-6",
        max_tokens=800,
        system=system_prompt,
        messages=[{"role": "user", "content": f"Evaluate this product idea critically: {idea}"}]
    )
    return role, response.content[0].text

def synthesize_analyses(idea: str, analyses: dict) -> str:
    analyses_text = "\n\n".join([f"## {role.upper()}\n{content}" for role, content in analyses.items()])
    response = client.messages.create(
        model="claude-opus-4-6",
        max_tokens=1500,
        system="You are a product strategy director. Synthesize multi-perspective analyses into a concise investment memo.",
        messages=[{"role": "user", "content": f"Idea: {idea}\n\n{analyses_text}\n\nWrite a 1-page memo: Verdict (Go/No-Go/Pivot), Key Strengths, Critical Risks, Next Steps."}]
    )
    return response.content[0].text

idea = "A subscription app that coaches users through difficult conversations (salary negotiations, conflicts) via real-time voice analysis."

with concurrent.futures.ThreadPoolExecutor() as executor:
    futures = [executor.submit(analyze_from_perspective, idea, role, prompt) for role, prompt in ANALYSTS.items()]
    analyses = dict(f.result() for f in concurrent.futures.as_completed(futures))

memo = synthesize_analyses(idea, analyses)
print(memo)

WarningImplementation — Low/NoCode

Stack: Typeform + Make + Claude API (4 parallel calls) + Notion

1. Input: A product idea is submitted via a structured Typeform.
2. Parallel Analysis: Make runs 4 parallel HTTP modules, each calling Claude with a different analyst persona.
3. Synthesis: Once all 4 analyses return, a 5th Claude call synthesizes them into the final investment memo.
4. Database: The memo, all 4 analyses, and the original brief are saved to a Notion “Product Ideas” database.
5. Scoring: Notion formula fields compute a composite score from keyword signals in each analysis.