Four techniques to improve AI agents | Rakesh Gohel

Last month, we saw 2 new techniques to improve AI agents Let's compare their effectiveness with other processes... Fine-Tuning and Agentic RAG have dominated the landscape. But Microsoft Agent Lightning and Anthropic Agent Skills are emerging as powerful alternatives with distinct advantages. 📌 Let me break down how these 4 strategies compare: 1\. Fine-Tuning Trains pretrained models on domain data to embed expertise into weights. - Goal: Domain adaptation and behavior change - Hallucination Control: Low to moderate - Best for: Tuning models for Specialized domains (healthcare, legal, finance) 2\. Agentic RAG Combines retrieval with autonomous agents for dynamic search and multi-step reasoning. - Goal: Dynamic information retrieval with reasoning - Hallucination Control: Moderate via verification - Best for: Complex queries needing exploration 3\. Agent Skills [NEW] Modular packages with instructions and scripts for executable capabilities on demand. - Goal: Modular expertise + executable capabilities - Hallucination Control: High through grounded execution - Best for: Domain tooling (PDF, Excel, code) 4\. Agent Lightning [NEW] RL-based training for continuous improvement through reward feedback. - Goal: Optimizing performance with RL - Hallucination Control: Improves via rewards - Best for: Iterative improvement of already deployed agents 📌 Which to choose? Choose Fine-Tuning when: - You have large domain-specific datasets and need the agent to internalize specialized knowledge permanently (medical diagnosis, legal analysis). Choose Agentic RAG when: - Your agent needs access to constantly changing information from multiple sources (research tools, competitive intelligence). Choose Agent Skills when: - Your agent needs to perform specific executable tasks like file manipulation, API calls, or code generation without complex infrastructure. Choose Agent Lightning when: - You have a deployed agent that needs to improve over time based on real