Simplifying Content Strategy with AI-Guided Workflows

One of the first small applications I built that leveraged an AI assistant was a tool that generated standardized content across platforms like social media, email, and websites. It worked fine, but it quickly became clear that in addition to just producing content, the app could also simplify the process that leads up to it.

As I tinkered with it, the app became more interactive, guiding a user step by step through the process of developing a cohesive content strategy while asking about campaign goals, audience, tone, and any other essential questions that need answers when developing content strategy. That structure made the process faster and also more thoughtful. Behind the scenes, I broke down the workflow into separate pieces with each handled by its own AI agent. If you're curious how something like that gets built, below is an overview of how I approached it.

The app works by dividing different parts of the content strategy process among separate AI agents in what’s sometimes referred to as agentic architecture. That means assigning each agent a specific responsibility, with all of them working together to complete a more complex task.

The result is a system that mirrors the way real teams collaborate. Each agent takes on a defined role, contributes its part, and hands off its output to the next. Here's how I structured the workflow:

• Campaign Planner: Gathers foundational inputs such as campaign goals, target audience, tone, and distribution platforms.
• Content Generator: Creates core messaging and platform-specific variations based on the campaign plan.
• Refiner: Adjusts tone, voice, and structure to match the platform and brand.
• SEO Mapper: Identifies keywords, hashtags, and metadata to improve visibility.
• Report Builder: Compiles everything into a structured summary that’s ready to share or export.

Once you have multiple agents involved, you need a way to keep them coordinated. That is the role of an orchestration layer. It manages the order of operations, passes outputs from one agent to the next, and ensures context is preserved throughout the workflow.

I used LangGraph to build this layer because it offered a clean way to chain tasks and manage memory, but there are other frameworks available like Haystack, CrewAI, and Autogen.

What I feel makes the app especially useful is how accessible it is. Whether a user is an experienced content strategist or a junior team member writing content for the first time, the tool helps each of them follow the same structured approach and achieve consistent results.

What surprised me most was how quickly the possibilities expanded once I started thinking in terms of multiple agents working together. Suddenly, you’re not just building tools, you’re creating systems that can coordinate, respond intelligently, and work together to build something much larger than any one part.