A conceptual framework for building multi-agent systems

Designing effective agentic systems requires a structured approach. There's a lot of content describing "how" to build agentic systems, but it’s important that we develop a conceptual understanding of "what" to build with agentic systems. Through our work with dozens of companies in Hypermode Agent Labs, we've designed an approach that transforms AI ambitions into business results.

After all, the purpose of AI is to automate work and remove toil. AI models are just one component of the system—the app itself must manage context, maintain state, and orchestrate complex workflows. Developers need to recognize that models alone can't independently execute tasks or retain memory—the surrounding architecture must provide that foundation to maintain context for the app to consume.

Cutting through the jargon

Before we dive in, let's clarify some terminology that often creates confusion for software engineers developing agentic systems:

• Agentic Flow: Business process you’re automating all or part of (e.g., customer support ticket resolution, content creation pipeline)
• Agent: Chunks of software that solve specific parts of a workflow. Often agents pass information back and forth between one another (hence, “multi-agent”).
• Context: Specific information about the problem you want the AI to solve—similar to how you'd give a new employee a handbook and access to the company wiki
• Tool: In code terms, functions or external services; in workflow terms; in workflow terms, "jobs to be done" (e.g., data retrieval, content generation)

Start at 100% quality, then automate

The most common failure mode we’ve observed when building AI systems is jumping straight to an autonomous system. AI systems can become very complicated very quickly– as a result, it becomes very difficult to know what changes most improve the quality of output. This approach might result in great demos, but delivers dangerous or unreliable outputs in the real world.

It's much easier to maintain quality by adding layers of intelligence incrementally. We recommend starting with a deeply understood "manual" process and incrementally delegating workloads to AI. Because these are non-deterministic systems, you may not always know why a model is failing, but it's important to at least identify which “black box” of the ten black boxes is causing trouble.

Where theory meets practice

We’ve been running Hypermode “Agent Labs” with founders and AI teams to help them nail their first AI use cases. These intensive workshops have resulted in production-ready systems that deliver measurable ROI.

1. Map out workflows in exhaustive detail

This is typically the most challenging part for team who haven't done much process engineering. This applies to software flows as well, but I’ll share a specific example about a business process we automated for clarity.

One of our customers creates thousands of custom assets for ad-tech companies. This typically requires multiple specialists (designers, copywriters, etc.).

We meticulously documented every step: gathering client color palettes, obtaining fonts, creating ad skeletons, collecting product metadata, and more. By visualizing the entire process, we identified areas where a significant amount of time was spent on repetitive tasks.

2. Replace specific sub-tasks with agents or tools

Ship incrementally—that’s our implementation approach. Put a tool or two in the hands of the humans running the workflow.

For our ad-tech customer, we built a Chrome extension that extracts product images from web pages, automatically labels them, and sends them to the next step—replacing the manual process of copying/pasting into Google Drive and then into Figma. This single tool reduced image preparation time.

Quick-win, quality-of-life features build support and momentum within organizations. The point of AI is to reduce toil— spend time doing what matters, not to making AI.

3. Cyborging (or enabling human-AI collaboration)

After building incremental tools for the human, run the workflow with humans as orchestrators and quality controllers. We use Vercel's v0 to build simple UIs that let humans ensure quality at each workflow step.

We created a "Tinder for outputs" interface where team members could quickly approve or reject AI-generated content, helping us calibrate which prompts and pipelines needed refinement.

4. Group tools and expose them to an orchestrator agent

By grouping 3-5 tools into a sub-workflow, you can validate how well an orchestrator agent reasons about goals with that toolset.

As an example, if you are automating the process of building personalized ad, group tools related to "selecting the optimal ad skeleton for specific product types." The orchestrator agent learned to choose appropriate templates based on product category, promotional status, and target audience—a task that previously required marketing specialists.

5. Wire it up

Expose your entire toolset to orchestrator agents but maintain human-in-the-loop before deploying outputs. Over time, you can reduce human involvement to sampling just 10% of outputs.

A financial services customer is using this approach to evolve their payments fraud prevention system—automating their workflows while maintaining accuracy.

6. Run your agent on full auto in prod

From concepts to production

Let’s face it: building multi-agent systems that actually work in production is hard. The gap between demo magic and systems that deliver real business value is wider than most realize. Avoid common pitfalls: tool proliferation, context collapse, evaluation oversight, overengineering. By following an incremental approach, our customers cut through the chaos to ship meaningful AI that their teams actually trust.

What we’ve learned through these Agent Labs is that successful agentic systems aren't built by pursuing the most advanced AI capabilities first. Instead, we’ve seen that they're built by deeply understanding your workflows, automating the right parts incrementally, and maintaining human oversight until you’ve earned the confidence to go fully autonomous.

Let us help you

If you are looking to turn your AI ambitions into reality, Hypermode’s Agent Labs provide the structured approach you need. We’ll help you:

• Identify the highest-impact, lowest-risk areas to explore AI
• Break down complex workflows into understandable, automation-ready components
• Design the right tools for your specific business context
• Build practical solutions using open-source technologies
• Gain confidence to scale from human-augmented to agentic systems

Ready to transform your workflows with AI? Reach out to learn more about Hypermode Agent Lab can turn your agent-building concepts into real-world solutions that deliver measurable ROI.

Let's build something that doesn't just demo well—it ships well.