Building an artificial intelligence system that is both effective and safe can often feel overwhelming for startup entrepreneurs, especially those bootstrapping their journey. As someone who has navigated the challenges of launching and growing multiple businesses in Europe, I recently explored a game-changing concept that combines modularity, safety, and scalability. Let’s talk about designing an “agentic AI system” using a control-plane architecture, a method gaining traction among those who want their AI to not only think but also act responsibly and adaptively. Here's why this approach matters and how you can implement it step by step.
Why You Should Care About Agentic AI
Unlike traditional AI, where systems perform pre-scripted tasks, agentic AI dynamically interacts with tools, adapts to new workflows, and even makes decisions. If you're running a business, this means you can delegate complex problem-solving tasks, like running customer support workflows or automating marketing decisions, to an intelligent agent. But with great power comes great responsibility. The control-plane architecture ensures those agents behave predictively and securely, which is vital to protect your brand and your customers’ trust.
Let's break this down for you.
What Does Control-Plane Architecture Offer?
At its core, a control-plane isn't just technical jargon. It's the "brain" overseeing how your AI communicates, manages tools, enforces safety protocols, and executes strategies. This approach is particularly appealing to entrepreneurs because you don’t have to reinvent the wheel. You can integrate modular components, adapt workflows as your business evolves, and remain confident that your AI won’t go rogue.
Some key benefits include:
- Safety measures: Prevent tools from being misused or overused.
- Transparency: Keep a log of what was done, how, and why, for easier debugging.
- Scalability: Add or remove tools based on your business growth.
These aspects become indispensable, especially when you're dealing with high stakes, such as intellectual property protection (near and dear to my heart as the founder of a CAD software startup) or customer data safety.
Step-By-Step Guide to Building an Agentic AI System
1. Start with Clarity About Your AI’s Purpose
Before jumping into coding, clarify what your AI system will actually do. Is it a virtual assistant, a tutor, or a budgeting analyst? Write down specific tasks and their outcomes. For example, if it’s an educational AI, define whether it explains concepts, assesses users’ understanding, or adapts content based on their progress. Start small!
2. Build a Knowledge Base
You can’t have reasoning without data. Think of your AI system as a new employee: they need training materials. Build a lightweight retrieval-augmented generation (RAG) system, this allows the AI to access a “knowledge bank” when users ask questions. Tools like NumPy and scikit-learn can quickly help you calculate similarities between user queries and existing documents.
Example technologies for this:
- Python libraries like NumPy for computations.
- Anthropic or OpenAI APIs for large language comprehension.
- Embedding storage for quick document searching.
3. Create Modular Tools
Instead of bloating your AI with every task under the sun, create modular “tools” that it can call upon when needed. For example:
- Search tools: Locate relevant information inside your database.
- Interaction logs: Store and analyze user activity to improve AI responses.
- Feedback generators: Provide quizzes, summaries, or reports based on user needs.
Make sure each tool is independently testable. This modular design isn’t just easier to debug, it also future-proofs your system.
4. Set Up the Control Plane
This is where all the magic happens. Your control plane should do the following:
- Validate every request: Are the tools being used correctly? Are resources being over-utilized?
- Orchestrate workflows: Direct your AI’s reasoning, deciding which tool to use and in what order.
- Log everything: Keep track of what’s happening under the hood to ensure transparency.
Use classes in Python to codify these controls. Here's a basic idea:
class ControlPlane:
def validate_action(self, action):
# Code to check security rules
def log_execution(self, details):
# Add logging details for later analysis
5. Integrate Agent Reasoning
Finally, you’ll need an "agent" that understands what it needs to do and how to do it. Equip it with a reasoning layer that follows these steps:
- Analyze the query.
- Consult relevant tools (via the control plane).
- Formulate a useful response.
Encourage it to "think out loud" in the logs so you can follow the decision-making process. Combine logic-building tools with APIs from companies like LangChain for streamlined multi-step execution.
Common Mistakes to Avoid
Entrepreneurs often jump headfirst into complex AI projects, which can lead to pitfalls. Be mindful of these missteps:
- Starting too big: Focus on a single workflow and master that before expanding. For example, if you’re building a recommendation system, test it with one category of products first.
- Ignoring safety: Not all AI mistakes are harmless. Build guardrails early to keep your system from making costly errors.
- Neglecting transparency: Always maintain logs and documentation, especially if your system makes decisions about sensitive matters, like financial data or customer service resolutions.
Lessons for Female Entrepreneurs in Europe
Funding challenges, hiring constraints, and cultural biases often add extra hurdles for women founders, especially in tech. But one thing I’ve learned is that modular and transparent systems, like a control-plane AI, can become a strong ally. Why? Because they allow you to “scale smart” without needing an oversized tech team or budget.
Moreover, being intentional about safety protocols not only builds trust but also positions you as someone who takes compliance seriously, something that can open doors to collaborations, especially with institutions looking for reliability, such as in the EU’s startup ecosystem.
The Future Potential for Startups
Now is the right time to invest in researching and experimenting with agentic AI designs. Companies like MarkTechPost and Google Cloud offer excellent resources to start your journey. Using these as guides, you can adapt their architectures to suit your business needs.
Final Takeaway
By embracing a control-plane approach to designing AI, startups can create systems that deliver trustworthy, scalable, and effective solutions. As a female entrepreneur, I encourage you to start small, leverage open-source tools, and layer your AI with safety and modularity. This hands-on method not only helps you grow lean but also equips your business for challenges down the road. After all, staying one step ahead is the key to turning ideas into thriving realities.
FAQ
1. What is agentic AI, and why is it important for startups?
Agentic AI enables systems to dynamically interact with tools, make decisions, and adapt to workflows, making it ideal for startups aiming to automate complex tasks with accountability and scalability. Learn more about agentic AI architecture
2. What is the role of a control-plane architecture in agentic AI systems?
A control-plane orchestrates AI systems by validating requests, managing tools, enforcing safety protocols, and logging actions for transparency. Explore control-plane architecture on Google Cloud
3. How can entrepreneurs start building an AI system with modular tools?
Start by creating independent tools that handle specific tasks like data retrieval, user profiling, or workflow management. Modular tools improve scalability and debugging ease. Check out modular AI tool frameworks
4. What are retrieval-augmented generation (RAG) systems, and why are they important?
RAG systems let AI access a knowledge base for reasoning, making them critical for answering user queries effectively with embedded data. Learn more about retrieval-augmented generation
5. How does safety get implemented in modular AI systems?
Safety is enforced through rules in the control-plane, which validate tool usage and resources while maintaining transparency via execution logging. Read about AI safety rules
6. What frameworks are recommended for agentic AI development?
Frameworks like LangChain enable modular workflows by integrating tools, APIs, and multi-step reasoning processes. Discover LangChain for AI workflows
7. How is transparency maintained in agentic AI systems?
Transparency is achieved through logging every request and workflow, enabling easier debugging and ensuring accountability in AI decisions. Explore transparent AI development
8. What industries can benefit from adopting agentic AI systems?
Agentic AI applies across sectors like healthcare, manufacturing, retail, education, and more by adapting to diverse operational needs. Explore industry applications on Google Cloud
9. How can startups scale AI systems effectively?
Using modular and scalable architectures like control-plane frameworks allows startups to adapt workflows, add tools, and expand safely as they grow. Learn more about scalable AI strategies
10. Where can I find coding resources for building agentic AI systems?
Code resources for control-plane and agentic AI systems are available on GitHub, including step-by-step implementations. Access agentic AI code libraries
About the Author
Violetta Bonenkamp, also known as MeanCEO, is an experienced startup founder with an impressive educational background including an MBA and four other higher education degrees. She has over 20 years of work experience across multiple countries, including 5 years as a solopreneur and serial entrepreneur. Throughout her startup experience she has applied for multiple startup grants at the EU level, in the Netherlands and Malta, and her startups received quite a few of those. She’s been living, studying and working in many countries around the globe and her extensive multicultural experience has influenced her immensely.
Violetta Bonenkamp's expertise in CAD sector, IP protection and blockchain
Violetta Bonenkamp is recognized as a multidisciplinary expert with significant achievements in the CAD sector, intellectual property (IP) protection, and blockchain technology.
CAD Sector:
- Violetta is the CEO and co-founder of CADChain, a deep tech startup focused on developing IP management software specifically for CAD (Computer-Aided Design) data. CADChain addresses the lack of industry standards for CAD data protection and sharing, using innovative technology to secure and manage design data.
- She has led the company since its inception in 2018, overseeing R&D, PR, and business development, and driving the creation of products for platforms such as Autodesk Inventor, Blender, and SolidWorks.
- Her leadership has been instrumental in scaling CADChain from a small team to a significant player in the deeptech space, with a diverse, international team.
IP Protection:
- Violetta has built deep expertise in intellectual property, combining academic training with practical startup experience. She has taken specialized courses in IP from institutions like WIPO and the EU IPO.
- She is known for sharing actionable strategies for startup IP protection, leveraging both legal and technological approaches, and has published guides and content on this topic for the entrepreneurial community.
- Her work at CADChain directly addresses the need for robust IP protection in the engineering and design industries, integrating cybersecurity and compliance measures to safeguard digital assets.
Blockchain:
- Violetta’s entry into the blockchain sector began with the founding of CADChain, which uses blockchain as a core technology for securing and managing CAD data.
- She holds several certifications in blockchain and has participated in major hackathons and policy forums, such as the OECD Global Blockchain Policy Forum.
- Her expertise extends to applying blockchain for IP management, ensuring data integrity, traceability, and secure sharing in the CAD industry.
Violetta is a true multiple specialist who has built expertise in Linguistics, Education, Business Management, Blockchain, Entrepreneurship, Intellectual Property, Game Design, AI, SEO, Digital Marketing, cyber security and zero code automations. Her extensive educational journey includes a Master of Arts in Linguistics and Education, an Advanced Master in Linguistics from Belgium (2006-2007), an MBA from Blekinge Institute of Technology in Sweden (2006-2008), and an Erasmus Mundus joint program European Master of Higher Education from universities in Norway, Finland, and Portugal (2009).
She is the founder of Fe/male Switch, a startup game that encourages women to enter STEM fields, and also leads CADChain, and multiple other projects like the Directory of 1,000 Startup Cities with a proprietary MeanCEO Index that ranks cities for female entrepreneurs. Violetta created the "gamepreneurship" methodology, which forms the scientific basis of her startup game. She also builds a lot of SEO tools for startups. Her achievements include being named one of the top 100 women in Europe by EU Startups in 2022 and being nominated for Impact Person of the year at the Dutch Blockchain Week. She is an author with Sifted and a speaker at different Universities. Recently she published a book on Startup Idea Validation the right way: from zero to first customers and beyond, launched a Directory of 1,500+ websites for startups to list themselves in order to gain traction and build backlinks and is building MELA AI to help local restaurants in Malta get more visibility online.
For the past several years Violetta has been living between the Netherlands and Malta, while also regularly traveling to different destinations around the globe, usually due to her entrepreneurial activities. This has led her to start writing about different locations and amenities from the POV of an entrepreneur. Here’s her recent article about the best hotels in Italy to work from.
About the Publication
Fe/male Switch is an innovative startup platform designed to empower women entrepreneurs through an immersive, game-like experience. Founded in 2020 during the pandemic "without any funding and without any code," this non-profit initiative has evolved into a comprehensive educational tool for aspiring female entrepreneurs.The platform was co-founded by Violetta Shishkina-Bonenkamp, who serves as CEO and one of the lead authors of the Startup News branch.
Mission and Purpose
Fe/male Switch Foundation was created to address the gender gap in the tech and entrepreneurship space. The platform aims to skill-up future female tech leaders and empower them to create resilient and innovative tech startups through what they call "gamepreneurship". By putting players in a virtual startup village where they must survive and thrive, the startup game allows women to test their entrepreneurial abilities without financial risk.
Key Features
The platform offers a unique blend of news, resources,learning, networking, and practical application within a supportive, female-focused environment:
- Skill Lab: Micro-modules covering essential startup skills
- Virtual Startup Building: Create or join startups and tackle real-world challenges
- AI Co-founder (PlayPal): Guides users through the startup process
- SANDBOX: A testing environment for idea validation before launch
- Wellness Integration: Virtual activities to balance work and self-care
- Marketplace: Buy or sell expert sessions and tutorials
Impact and Growth
Since its inception, Fe/male Switch has shown impressive growth:
- 5,000+ female entrepreneurs in the community
- 100+ startup tools built
- 5,000+ pieces of articles and news written
- 1,000 unique business ideas for women created
Partnerships
Fe/male Switch has formed strategic partnerships to enhance its offerings. In January 2022, it teamed up with global website builder Tilda to provide free access to website building tools and mentorship services for Fe/male Switch participants.
Recognition
Fe/male Switch has received media attention for its innovative approach to closing the gender gap in tech entrepreneurship. The platform has been featured in various publications highlighting its unique "play to learn and earn" model.
