2025 feels like the dawn of the AI Agent revolution—a time when software isn’t just smart, but autonomous enough to carry out tasks, make decisions, and improve itself with minimal human input. If you’re a developer or founder looking to get ahead of the curve, building AI agents is one of the most exciting frontiers in tech right now.

Whether you’re a seasoned engineer or diving into AI for the first time, this guide will outline best practices for developing AI agents that don’t just work but thrive in real-world applications.

1. Start with the Problem, Not the Technology

The most common pitfall for developers is getting swept up in the tech and forgetting the end user. Before diving into building, identify a specific problem or inefficiency your AI agent will solve.

For example, Fairgo—an AI HR agent—was built to streamline hiring processes by automating initial candidate interviews and recommendations. Its success lies in its laser focus on solving a pain point for recruiters, not just showcasing the capabilities of AI.

Tip: Spend time talking to potential users. Understand their workflows and pain points deeply before writing a single line of code.

2. Choose the Right Foundation

Not all AI agents are built the same, and your choice of foundational model matters. Popular options include:

• OpenAI’s GPT models for general-purpose natural language processing.
• Relevance AI for domain-specific data workflows.

Understand what your agent needs to achieve. For complex, industry-specific tasks, you may need fine-tuned models. For broader, text-based applications, pre-trained models may suffice.

Tip: Keep scalability in mind. Choose a platform that allows easy iteration and adaptation as your agent evolves.

3. Embrace No-Code and Low-Code Tools

AI agents don’t have to cost millions to build. Platforms like Bubble.io and Flutterflow make it easier than ever to create functional prototypes without deep technical expertise.

At Mayfly Ventures, we’ve seen startups validate their AI agents in weeks using low-code tools, saving tens of thousands in development costs. Your first iteration doesn’t need to be perfect—it needs to be viable.

4. Prioritize Context and Fine-Tuning

The power of AI agents lies in their ability to understand context. Generic models like GPT-4 are powerful but often lack the industry-specific nuance required for vertical AI agents.

Fine-tuning your model with domain-specific data is key. For instance, training a sales AI agent on scripts, common objections, and CRM data will make it far more effective than relying solely on out-of-the-box models.

Tip: Use small-scale pilot programs to gather data and fine-tune incrementally based on real-world feedback.

5. Build for Explainability and Trust

AI agents operate in environments where trust is non-negotiable. Whether it’s healthcare, finance, or customer support, users need to understand how and why your agent makes decisions.

To build trust:

• Incorporate explainable AI techniques.
• Provide detailed logs or summaries of decisions made by your agent.
• Focus on transparency, especially when handling sensitive data.

6. Integrate Seamlessly into Existing Systems

An AI agent’s value lies in how well it complements existing workflows. For example, Relevance AI offers APIs that can easily integrate with no-code tools, CRM platforms, and even legacy systems.

Avoid building an AI agent in isolation. Make it compatible with the tools and processes your target users already rely on.

7. Test, Iterate, and Improve

The best AI agents are built through iteration. Launch a minimum viable product (MVP) with a narrow feature set, then gather feedback from users to improve functionality.

Example:

When Mayfly Ventures built VOLI’s AI-powered MVP, the focus was on proving its ability to solve one core problem. Iterative updates, guided by user feedback, led to a robust platform that’s now onboarding enterprise customers.

8. Design for a Human-Agent Collaboration

AI agents don’t replace humans—they amplify them. Design your agent to complement human workflows, not to replace them entirely.

For example:

• An AI agent in customer support might draft responses for human agents to review, rather than sending them automatically.
• A sales AI agent could prioritize leads, leaving the final decision-making to a human sales team.

9. Plan for Ethical Use and Compliance

Ethics in AI is no longer optional. Be mindful of potential biases in your model, and ensure compliance with data privacy laws like GDPR or Australia’s Privacy Act.

Proactively address these concerns by:

• Auditing training data for bias.
• Allowing users to opt out of data collection.
• Creating clear policies for how your AI agent operates.

10. Focus on Long-Term Maintainability

The work doesn’t stop once your AI agent is live. Maintenance, updates, and retraining are ongoing processes.

Invest in:

• Clear documentation for developers and users.
• Automated monitoring systems to track performance and identify issues.
• A scalable infrastructure that can handle increased demand.

Conclusion

Developing AI agents isn’t just about building—it’s about solving real problems, iterating based on user feedback, and ensuring long-term usability. Whether you’re a solo founder or part of a venture studio, following these best practices will help you build AI agents that deliver real value and stand out in the market.

At Mayfly Ventures, we specialize in helping founders build scalable AI agents using low-code tools and cutting-edge models like Relevance AI. If you’re ready to turn your idea into reality, let’s talk.

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