Before You Build an AI Agent, Read This

2026-01-17 · Source: Learn AI Together · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, medium

Summary

Towards AI Co-founder & CTO Louis-François Bouchard recently presented at the Moroccan Data Scientists (MDS) organization, discussing agentic AI architectures. His talk, now available as a video, emphasizes the importance of basic workflows and how Towards AI decides between using workflows, tools, agents, or multiple agents, providing clear examples. The presentation also includes a free "agents cheatsheet." Additionally, the brief highlights community activities, including a Discord channel for learners, an AI poll showing a preference for video-based learning and building, and several collaboration opportunities for projects involving AI automation, personalized LLM agents, and interview preparation. The TAI Curated Section features articles on building LLMs from scratch with PyTorch, graph databases, robust RAG systems, adaptive retrieval routers, continuous autoregressive language models, and memory implementation for AI agents with AWS Bedrock AgentCore Memory.

Key takeaway

For Machine Learning Engineers building AI agents, prioritize understanding the underlying workflows and retrieval mechanisms before diving into complex agentic architectures. Your focus should be on implementing robust data handling, such as advanced chunking and hybrid search for RAG, and considering adaptive retrieval routers to enhance system reliability. Explore managed services like AWS Bedrock AgentCore Memory to address statelessness and ensure conversational continuity in your agent designs.

Key insights

Effective AI agent development prioritizes foundational workflows and adaptive retrieval over static, single-tool approaches.

Principles

• Workflows precede agents
• Adaptive retrieval improves agent reliability
• Memory is crucial for agent statefulness

Method

Implement advanced chunking, hybrid search, and reranking for RAG systems. Use an adaptive retrieval router with feedback loops to dynamically select search strategies for agentic AI.

In practice

• Utilize semantic and hierarchical chunking for RAG.
• Explore Neo4j for highly connected data.
• Implement AWS Bedrock AgentCore Memory for agent state.

Topics

• AI Agents
• LLM Architectures
• RAG Systems
• AI Workflows
• Agent Memory Management

Best for: AI Student, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Learn AI Together.