On February 27, 2025, ArcBlock hosted a two-hour AI Agent workshop for 30 developers and builders in a Seattle tech space. The hands-on session aimed to teach practical AI agent-building using their no-code AIGNE platform. Attendees explored Model Context Protocol (MCP), prompt engineering, and built real-world apps like translation and meeting summarizer agents.
- 03:26 Defining AI Agents and Its Applications
- 10:58 Knowledge and Memory in AI Agents
- 22:42 Prompt Engineering Essentials
- 37:15 Entry Agent as a User Interface
- 40:05 LLM Cost Controls and Efficiency
- 43:32 Validating LLM Responses
- 45:48 Using Multi-Agent Configurations
- 47:26 Chain of Density Prompting
- 49:26 Cost Management in AI Agents
- 57:39 Agent Walkthroughs
- 01:21:53 Model Context Protocol (MCP) and Future Directions
Workshop Recap#
Understanding AI Agents#
The workshop explored two primary approaches—workflows and agentic AI. Workflows involve pre-defined sequences of tasks, while agentic AI consists of autonomous entities capable of making decisions and adapting based on input. Agentic AI was particularly emphasized as being adept at handling open-ended problems.
Best Practices in Building AI Agents#
One of the core discussions focused on strategies to build efficient and effective AI agents. Key practices included:
- Modular Agent Design: Assign a specific purpose to each agent to maintain clarity and effectiveness.
- Prompt Engineering: As a no-code platform, clarity in prompts is crucial. Specificity and detailed task breakdowns help agents perform optimally, defining expected outputs and handling scenarios where data might be missing.
Advanced Prompting Techniques#
The "Chain of Density Prompting" enhances responses' depth and human-likeness by iterating summaries multiple times. This is a great technique for anyone aiming for high-quality output without multiple agents checking each other's work.
AI Agent Examples and Step-by-Step Guides#
The session included practical demonstrations of building real-time applications such as:
- Translation Agents: Highlighted a two-agent configuration involving direct translation followed by paraphrasing to ensure accuracy and fluency.
- Learning Plan Creator: Showcased a three-agent system to assess a user's skills and preferences to create personalized learning plans, demonstrating the possibilities of dynamic and user-friendly AI applications.
- Meeting Summarizer: A three-agent setup for cleaning, summarizing, and extracting action items from meeting transcripts, illustrating how AI can streamline business operations.
Managing AI LLM Costs#
Cost control was another critical topic, emphasizing the importance of being concise, choosing the right models, and utilizing features like caching to manage expenses effectively.
The Future with MCP#
The introduction of Model Context Protocol (MCP) was a highlight, promising supercharged abilities for AI agents to integrate seamlessly with external systems and platforms like Stripe. This protocol represents a step towards more versatile and interconnected AI ecosystems, promising vast possibilities for developers.
Conclusion#
In conclusion, the AIGNE workshop was a treasure trove of insights, best practices, and futuristic outlooks for AI builders. It provided invaluable guidance for leveraging decentralized AI platforms to harness the full potential of no-code applications. Whether it's about improving translation accuracy or crafting bespoke learning experiences, the workshop