Generative AI (gen AI) has moved quickly from novelty to necessity—and one of its most promising frontiers is the rise of agentic systems. As companies look to scale their operations with more intelligence and flexibility, the idea of using AI agents—systems that can act with purpose, context, and collaboration—is becoming a game-changer. Rather than relying on isolated tools, companies are increasingly looking to systems of coordinated, intelligent agents to tackle complex workflows with greater precision and flexibility. Today, we’re sharing a high-level overview of one of our most recent projects: the delivery of a multi-agent AI content generator system to help our client create high-quality, on-brand content at scale.
The challenges we are solving
When our client approached us, their content pipeline was slow, inconsistent, and under pressure. It took days—sometimes over a week—to deliver a single blog post. Tone and formatting varied depending on the writer. SEO felt like guesswork. Approvals and fact-checking caused bottlenecks, and internal experts were constantly pulled in to answer the same questions.
The team had heard of AI tools, but adoption was low. Skepticism was high. They needed more than a tool: they needed a system that could scale high-quality content creation and adapt to the complexity of their publishing workflow. That’s what led us to design an intelligent, modular solution built around AI agents.
What is Agentic AI?
Before we dive into our architecture, let’s clarify what we mean by « agent » in this context. In the world of AI, an agent is a focused, goal-oriented system that can observe, reason, and act—often autonomously. These agents are typically designed to perform specific tasks, make decisions based on context, and collaborate with other agents or humans as part of a larger workflow. Put simply, agents act as intelligent virtual collaborators that organisations can deploy to handle tasks in a scalable way.
This emerging model—often referred to as agentic AI—is gaining momentum as one of the most powerful ways for organisations to scale automation while maintaining control and flexibility. For many companies, adopting an agentic architecture will become a key strategic initiative in the years ahead.
Why we chose a multi-agent approach
Generating specialised content at high quality is essential for our client—a challenge that simply can’t be solved by a marketer typing a few prompts into ChatGPT and hoping for the best. To deliver real value and achieve their ultimate goal of ranking well on Google and driving conversions, we knew from day one a single AI agent wouldn’t cut it.
At high level, the content creation workflow is inherently complex. Most of the time, it includes:
- Research, information retrieval and identification of products to promote
- Briefing
- Drafting
- SEO optimisation
- Visual selection
- Internal and external linking
- Final publishing and social sharing
Each of these steps demands different capabilities, and sometimes, different oversight. A single agent attempting to do it all would be clumsy and hard to debug. Instead, we designed a multi-agent AI architecture, where each agent is specialised, modular, and easy to control and monitor.
This structure also made it easier to insert human-in-the-loop checkpoints at critical moments—giving the client control without slowing them down. The result: a scalable, AI-driven publishing workflow that’s transparent, flexible, and fast.
Meet the agents behind the system!
We’ll dive deeper into each of these in future posts, but here’s a quick overview of the cast:
- Brief Writer Agent: This agent transforms campaign goals or rough ideas into a structured, detailed content brief. It ensures the AI writers have the strategic context they need, removing ambiguity and bringing consistency to how ideas are executed across teams. No more staring at blank pages or misinterpreting briefs.
- Writing Agent: Powered by tailored prompts and refined brand tone rules, this agent creates the first version of the content based on the brief. Its job is not just to write, but to write with relevance, fluidity, and tone that matches the brand. It replaces hours of drafting with high-quality, ready-to-refine output.
- RAG Agent (Retrieval-Augmented Generation): Accuracy and depth matter, this agent taps into the client’s internal documentation, resources, and past content to enrich the writing. It pulls real data and facts into the generation process—avoiding hallucinations and aligning output with expert knowledge.
- Content Connector Agent: This agent helps connect the dots. It analyses the content, then intelligently recommends and embeds links to relevant internal posts, product pages, and trusted external sources. It boosts SEO, improves user journeys, and removes the manual hunt for the right links.
- Image Selector Agent: This agent matches content with the right visuals. It scans the client’s image library, uses tags and metadata, and suggests visuals that align with the topic and tone. This ensures every piece is visually engaging, without bottlenecking the creative team.
- Publisher Agent: Once content is finalised, this agent prepares and pushes the post directly to the CMS—formatting, tagging, and applying metadata as needed. It takes care of the final mile of publishing so the team can move fast without skipping crucial steps.
Each agent plays a clear role in a well-choreographed workflow, reducing errors and freeing up the team to focus on high-value activities. This agent-based AI system is the backbone of our client’s new enterprise content automation strategy.
Our delivery method
We approached this project with the same energy and urgency we bring to all of our work—move fast, learn faster, deliver value early.
Here’s how we tackled it:
Each sprint outlined below was part of an iterative journey. In this post, we’re giving a high-level overview of each phase. We’ll explore every sprint in greater depth in upcoming posts to share more lessons, decisions, and examples from the build.
Sprint Zero: Discovery Workshop
We began with a collaborative session to understand the content flow, identify blockers, and define OKRs. This gave us the context to design an effective AI system and align on business outcomes.
Sprint 1: The MVP
We launched with a lite version of the Brief Writer and Writing Agents—just enough to show how AI could help generate draft content quickly. We also introduced a feedback loop from the start to build trust and refine outputs.
Sprint 2: The RAG MVP
This sprint focused on boosting content accuracy using Retrieval-Augmented Generation. We explored the client’s data landscape, implemented a vector database, and built a knowledge base to support content generation.
Sprint 3: Brand Tone & Prompt Chaining
Sprint 3 tackled tone and quality. We documented brand voice with the team and added prompt chaining to refine drafts post-generation. This made outputs feel far more polished and on-brand.
Sprint 4: Content Connector Agent
To enhance SEO and relevance, we introduced the Content Connector Agent. We built a structured database of existing content and helped the team define trusted external sources. The agent could now intelligently weave in internal and external links.
Sprint 5: Visuals with the Image Selector Agent
Visual automation took center stage in Sprint 5. We cleaned up and tagged the media library so the Image Selector Agent could find and suggest the right visuals. This made posts more engaging with minimal manual work.
Each sprint was structured for learning and impact: test, improve, repeat.
AI Change Management in Action
This wasn’t just a tech delivery. It was an AI transition—and like any successful transformation, it required thoughtful change management. We use the ADKAR model (Awareness, Desire, Knowledge, Ability, Reinforcement) as a guiding framework to support adoption and build lasting confidence in the new system:
- Awareness :We made sure the team understood why this change was happening and how it aligned with business goals.
- Desire: Through early involvement and clear communication, we built genuine enthusiasm around how AI could make their work easier.
- Knowledge: We provided structured training, playbooks, and live demos to show exactly how the system worked.
- Ability : Guardrails, override controls, and human-in-the-loop checks helped the team feel empowered, not replaced.
- Reinforcement: Regular Q&A sessions, progress reviews, and visible wins helped embed trust and encourage continued use.
This helped shift the perception of AI from a mysterious “black box” to a reliable, transparent co-pilot. People didn’t just accept the system—they embraced it. This is what effective change management in AI adoption looks like.
The Results
In under two months, the client went from a clunky manual process to an AI-powered content engine. The results spoke for themselves:
- Content creation time dropped from days to 45 minutes
- Writers could start from richer, more structured briefs
- Team input shaped the system from the start, increasing adoption
- A clear foundation was built for future agents and automation
By starting simple and building iteratively—with feedback baked in—we delivered a system that worked for the team, not just around them.
The Journey Continues
While these first five sprints laid a strong foundation, the project is still ongoing. We’re continuing to explore and test new capabilities based on real team needs and feedback. What’s remarkable is that each sprint isn’t pre-defined months in advance—we’re test-and-learn by design.
At the end of each sprint, we reflect on what worked, identify new opportunities, and define the next set of sprint goals together with the client. This agile, collaborative approach ensures that the system keeps evolving in lockstep with the team’s maturity, content goals, and business priorities. It’s how we’re building a future-proof approach to enterprise content automation.
What’s Next in the Series
This is just the beginning. In upcoming posts, we’ll go deeper into:
- How each agent was designed and trained
- How we approached prompt engineering for accuracy and tone
- Lessons learned from scaling RAG in a real publishing workflow
- Building systems that invite human creativity instead of replacing it
At Meadow Brooke, we don’t do bloated decks or theoretical strategies. We build AI systems that work—fast, user-friendly, and built for real-world adoption.
