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10 most important lessons we learned from 6 months building AI Agents
r/aiagents
8/15/2025
Content Summary
The post discusses 10 key lessons learned from building an AI agent platform called Kadabra over six months. The author shares best practices such as starting with a prompt skeleton, making prompts modular, using simple markers for debugging, executing one tool at a time, clarifying unclear requests, separating updates from questions, logging the entire workflow, validating structured data, treating tokens like a budget, and scripting error recovery. Comments include discussions around pricing models, with some users challenging the hybrid approach and suggesting alternative methods.
Opinion Analysis
Mainstream opinion supports the hybrid pricing model, emphasizing the need to balance collaboration and compute costs. Some users argue against per-seat pricing, suggesting it may limit adoption. There is debate around whether usage-based pricing could be more flexible without compromising revenue. Most agree on the importance of modularity, structured data validation, and error recovery in AI agent development. Community engagement and transparency in decision-making are seen as key growth factors.
SAAS TOOLS
| SaaS | URL | Category | Features/Notes |
|---|---|---|---|
| Kadabra | http://getkadabra.com/ | AI Workflow Automation | Plain language "vibe automation" that turns chat into drag & drop workflows, similar to N8N × GPT |
| FlowMetr | https://FlowMetr.com | Workflow Monitoring | A workflow monitoring solution mentioned in a comment |
USER NEEDS
Pain Points:
- Difficulty in managing complex AI agent workflows
- Challenges with prompt engineering and modularity
- Need for structured data validation and error recovery
- Concerns about pricing models for AI agents
- Need for better user guidance and interaction design
Problems to Solve:
- Creating clear and modular prompts for AI agents
- Ensuring reliable tool execution and error handling
- Managing token usage and cost efficiently
- Balancing per-seat and usage-based pricing models
- Improving user experience through better communication
Potential Solutions:
- Using modular prompts with separate files for identity, capabilities, safety, and tools
- Implementing clear markers for output parsing and debugging
- Limiting tool use to one step at a time for clarity and reliability
- Adding error recovery mechanisms like retry and escalation strategies
GROWTH FACTORS
Effective Strategies:
- Hybrid pricing model combining seats and usage credits
- Focusing on both collaboration and compute usage
- Testing different pricing structures (e.g., capacity units, per active workflow)
- Emphasizing product-market fit by addressing user needs and pain points
Marketing & Acquisition:
- Engaging with the community on platforms like Reddit
- Sharing war stories and lessons learned to build credibility
- Encouraging user feedback and discussion
Monetization & Product:
- Hybrid pricing strategy to balance collaboration and compute costs
- Offering flexibility in pricing models to cater to different user segments
- Focusing on scalability and reliability of the product
User Engagement:
- Encouraging discussions and feedback through comments
- Providing detailed explanations of design decisions
- Highlighting the value of the product through real-world use cases