Agent Development
The Agent Development Journey
A Sculptor's Approach to AI
Building an effective AI agent is more like sculpting than painting on a blank canvas. Rather than scripting every possible interaction from scratch (as with traditional NLU-based systems) or hoping a basic system prompt will suffice, Parlant advocates for starting with a foundation and progressively refining it.
Finding the Sweet Spot
Traditional approaches to AI agents sit at two extremes. On one end, NLU-based systems offer complete control but require exhaustive scripting of every interaction. On the other, basic LLM implementations with system prompts offer minimal effort but limited control over agent behavior.
Parlant strikes a balance between these extremes. Start with the natural capabilities of an LLM, then sculpt its behavior through iterative refinement using guidelines, glossary terms, context variables, and tools.
The Iterative Development Cycle
- Begin with the Base: Start with a basic agent implementation and observe its natural behavior.
- Identify Key Gaps: Monitor interactions and note where the agent's responses don't align with your expectations.
- Progressive Refinement:
- Add glossary terms to clarify domain understanding
- Implement guidelines to shape specific behaviors
- Integrate tools for dynamic functionality
- Set context variables for personalization
Embracing Reality and Progress
Perfect shouldn't be the enemy of good. One builder shared an illuminating perspective: they were delighted with an AI agent achieving 70% accuracy in resolving their cases, because their human agents were only managing 50% in practice. However, with Parlant's structured approach to agent development, you can and should aim for 90%+ accuracy.
The Feedback Loop
Success comes through iteration:
- Deploy your agent with basic capabilities
- Gather feedback from real interactions
- Consult with business experts about edge cases
- Implement refinements through guidelines and tools
- Test changes in a staging environment
- Deploy and monitor improvements
Think of it like training a new employee: they start with basic capabilities, and through feedback and guidance, they progressively improve their performance. The key difference? Once you've refined a behavior in Parlant, it stays refined—no retraining needed.