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AI Agents, APIs & MCP Integrations
Original framework • Capability overview — not a client case study.
A controlled integration blueprint for agentic workflows that connect models to approved tools, data, and business systems through APIs, webhooks, function calling, and MCP servers.
The design defines task boundaries, decision points, tool contracts, data exchanges, authentication concepts, workflow state, retries, error paths, logging, evaluation, and human approval checkpoints.
Typical outputs include an orchestration graph, API and MCP integration map, tool schemas, guardrails, and an implementation plan. The goal is useful automation with permission boundaries, observability, and human oversight—not autonomy for its own sake.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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What if a Gen AI tool let you see what’s happening behind the process?
This weekend I had the chance to try out ComfyUI.
I expected the usual node-based canvas, but the tool feels more like visual programming. Instead of hiding most of the process behind a simple UI, it exposes the workflow itself.
And that’s what makes ComfyUI both interesting and intimidating.
I’m still at the early stage of figuring out this tool, but I can see it being especially useful for creatives who want precise control over their AI workflows and the ability to build their own pipelines.
I included a process walkthrough in the comments.
Have you tried ComfyUI before?
Designed and built the core product experience for Beauto, an AI-powered automation tool for small businesses.
What I did:
AI Assistant — chat interface where users describe a task and the AI builds the automation, with proactive, impact-ranked suggestions
Integrations hub — searchable library of one-click connections (Calendar, Stripe, HubSpot, Slack, etc.)
Analytics dashboard — automations run, hours saved, and conversion rate at a glance
Result: A chat-first flow that felt far less intimidating than traditional automation builders in user testing, driving most new automations created.
So creative 👏
AI Digital Marketing Strategy Architect Design (Draft 1 of Project)
AI Digital Marketing Strategy Architect application. Here is a high-level overview of the project's architecture and features:
Core Functionality: The application is a comprehensive 4-phase digital marketing strategy builder that helps users plan, build, and automate their growth engines. It's designed with an elegant, editorial-style interface featuring high-contrast typography and clean layouts.
Phase 1: Foundation: A module for establishing the product's niche, generating detailed target personas using AI, and setting up the foundational tech stack (analytics, landing pages, email lists).
Phase 2: Content Engine: Focuses on organic reach. It includes an interactive SEO topic cluster generator, a visual ContentMap graph to visualize pillar links, and an AI-powered repurposing studio that transforms a single article into multiple formats (threads, LinkedIn posts, video scripts).
Phase 3: Growth & Acceleration: Built for paid and organic scale. Features a paid ad testing matrix with AI "Scale vs Kill" rules, an organic social winner analysis simulator, and an Answer Engine Optimization (AEO) readiness auditor.
Phase 4: Scale & Automation: Focuses on workflow automation. Includes a visual Zapier/n8n pipeline builder, a live lead simulator for AI-personalized email drafting, and an A/B testing loop optimizer.
Technical Architecture: A full-stack application utilizing React on the frontend and an Express server on the backend to securely manage Gemini API calls. It persists project state locally, includes preset templates (like SaaS or E-commerce), and offers a robust export engine to generate Markdown or Raw JSON blueprints.
Clean work