When our client came to us, their content pipeline was slow, inconsistent, and under strain. Producing a single blog post could take over a week. Tone and formatting varied between writers. SEO felt like guesswork. Approvals, fact-checks, and constant input from subject-matter experts created frequent bottlenecks.
The team had experimented with AI tools—but adoption was low and skepticism was high. They didn’t need just another writing assistant. They needed a system: something reliable, intelligent, and flexible enough to handle real-world publishing complexity.
To solve this, we designed a modular AI content engine powered by specialised agents—each one purpose-built for a step in the content workflow. But we didn’t build it all at once. Instead, we used a 5-week sprint approach, refining the system incrementally and learning fast.
Our approach:
To move fast and deliver real results, we ran this project as a series of tightly scoped, high-impact sprints. Each sprint was more than a milestone, it was a learning opportunity. We didn’t aim for perfection up front. Instead, we built iteratively, refined based on real feedback, and improved as we moved.
Here’s how five focused sprints transformed a slow content process into a modular, AI-powered publishing engine:
Sprint 1: Discovery & mapping workshop
Every great system starts with clarity. We began with a series of collaborative workshops to immerse ourselves in the client’s existing content creation flow. This wasn’t just process mapping—it was a deep dive into how ideas moved (or got stuck) from inception to publication.
We brought together marketers and a SEO specialist to create a shared picture of reality. Where were the bottlenecks? Who held the key knowledge? What did “good” look like?
What we uncovered was telling: content creation was slow not because people weren’t trying—but because they were starting from a place of ambiguity and lack of resource. Because of the lack of focus, briefs were vague. Strategy was disconnected from execution and too much was being reinvented and done manually / inconsistently with every post.
Outcome:
At the end of the sprint, we were ready to start building the agent system with:
✅ A clearly mapped end-to-end content process, including handoffs, delays, and decision points
✅ A prioritised set of opportunities where AI could truly help.
Key Learning:
This sprint gave us the insight that defined everything else: the content machine wasn’t broken—it lacked alignment and structure. That’s why our first agents wouldn’t be writing long-form content—they’d be laying the groundwork.
Sprint 2 – The MVP: Brief writer + content agent
Armed with clarity, we moved into action. In this sprint, we built and launched lite versions of two foundational agents:
-
A Brief writer agent, to generate structured, goal-aligned content briefs
-
A Content agent, designed to draft blog posts directly from those briefs
Our goal here wasn’t perfection—it was proof. Could this system work? Would the team use it? Could AI play a meaningful, repeatable role in content creation?
To maximise learning, we implemented a built-in feedback loop: the marketing team would test outputs, rate them, and leave comments. This closed the gap between technical capability and human trust.
⚡️ Outcome:
✅ Two live agents running in production, creating SEO-ready drafts in <10 minutes
✅ Human-AI collaboration began to take shape, with trust growing
Key Learning:
Speed was impressive! However (not surprisingly) early drafts varied in depth. Without a connection to internal knowledge, the content sometimes missed nuance and was too generic. It lacked the field expertise that is one of the key value propositions of the organisation. That insight defined our next focus: grounding the AI in real field expertise.
Sprint 3 – RAG for trusted Knowledge
We demonstrated that AI could write. But now it needed to write with depth and expertise. This sprint introduced Retrieval-Augmented Generation (RAG)—a method that lets agents pull factual, contextual information during content generation.
We created a vector database and curated a custom knowledge base from internal documents, past blogs, product sheets, and expert Q&A material. The goal was to make the AI smart enough to reference trusted materials on demand.
The RAG Agent now enriched drafts with real company data, aligning them with known facts, brand messaging, and previously published thought leadership.
📚 Outcome:
✅ Consistently accurate content, embedded with context only the business could provide
✅ Reduced hallucinations, improved fact-checking, and greater team confidence
Key learning:
With information in place, quality and trust soared. But another issue emerged: tone. The content was now correct, but it didn’t always sound like the brand. In some ways, it felt robotic. That set the stage for our next refinement.
Sprint 4 – Brand Voice & Prompt Chaining
It was time to add personality!
In this sprint, we worked with the client’s marketing to document voice guidelines and tone-of-voice preferences. We then embedded these into the system through a combination of prompt refinement and prompt chaining—a technique that allows outputs to be progressively enhanced through multiple logic stages.
Rather than dumping everything into one mega prompt, each agent followed a structured writing process: initial draft → tone adjustment → formatting polish. We also identified that different types of content required different prompts to deliver the quality expected. So we branched the prompts accordingly.
The result? Content that felt like it came from the in-house team.
Outcome:
✅ Drafts were no longer just accurate—they were authentically on-brand
✅ The team started recognising their own voice in AI-generated work
Key Learning:
This sprint taught us that brand tone isn’t a cherry on top—it’s foundational to adoption. When content feels right, teams engage more. We also learned that breaking prompts into logical steps was key to more human-like writing.
🔗 Sprint 5: Internal link agent
Finally, we addressed a pain point that had been hiding in plain sight: linking. Writers were manually hunting down internal links for SEO and related reading—an error-prone, time-consuming chore.
We built an Internal Link Agent that could scan drafts, understand topic relevance, and insert links to appropriate internal and trusted external content. To support it, we helped the client build a structured content inventory and tagging system.
Even in its first iteration, this agent saved hours and improved SEO hygiene across the board.
🔗 Outcome:
✅ Posts were now published with smart, contextual links, automatically
✅ The Marketing team gained consistency, freed from tedious searches
Key Learning:
Sometimes, the most valuable automations aren’t glamorous. They’re the ones that remove friction quietly but completely. This agent turned a repetitive task into a strength and brought us closer to a fully scalable pipeline.
Each sprint made us smarter. Each outcome brought the system closer to reality. And by the end of the fifth sprint, the team wasn’t just adopting the system—they were asking for more.
But the project didn’t end there.
This 5-week sprint sequence was just the foundation. With the core agent system in place, we’ve continued to evolve and expand the architecture—refining workflows, introducing new capabilities, and deepening the system’s intelligence based on real usage and feedback.
We’re sharing this sprint journey first because it shows what’s possible when you take an iterative, outcome-driven approach to AI implementation. It’s not about waiting for the perfect build—it’s about learning in motion and building systems that are useful from day one.
In our next article, we’ll take you behind the scenes of the full multi-agent system: how each agent was designed, how data flows between them, and how we approached knowledge management, oversight, and scalability. We’ll also share what’s happened since the sprints, and how the system continues to grow with the client’s needs.
Stay tuned for the next part of the series. This is how we make AI work in the real world—one smart sprint at a time.
