This episode discuss Agent Bricks, a new Databricks product designed to simplify and automate the development of high-quality, domain-specific AI agents by handling evaluation and optimization. Agent Bricks aims to overcome challenges in agent development like difficult evaluation, complex "knobs," and cost-quality trade-offs, enabling faster deployment. The sources also mention DSPy 3.0, a programming framework for language models that allows for modular AI system creation and optimization, contrasting it with Agent Bricks by highlighting its open-source nature and deeper coding requirements. Finally, SuperNetiX, an upcoming agent framework powered by DSPy from Superagentic AI, is introduced as an alternative that offers more flexibility and is not tied to specific commercial platforms like Databricks.

Agent Engineering, a new discipline focused on designing, developing, and supervising intelligent AI agents. These agents, powered by Large Language Models (LLMs), are distinct from traditional software as they are autonomous, goal-oriented entities capable of perceiving, reasoning, acting, and learning. The field emphasizes intent-aligned agents built with components like integrated LLMs, adaptive planning, and centralized memory, contrasting with current prompt-based LLM interactions that lack robust abstraction. It highlights the shift from hardcoded logic to orchestrating intelligent systems, emphasizing the need for better specifications, rigorous evaluation, and new professional roles to manage these evolving AI paradigms. The text concludes by asserting that Agent Engineering is a fundamental change in software development, fostering collaborative autonomy between humans and AI.

"Agent Experience," or AgentEx, represents the evolution of experience design, shifting focus from human users (UX) and developers (DevEx) to autonomous AI agents. This new discipline addresses the unique needs of AI agents as they increasingly interact with and build digital systems, emphasizing the importance of creating environments and interfaces that agents can effectively understand and utilize. Key trends driving AgentEx include the rise of agent-to-agent communication standards like the Model Context Protocol (MCP), the transition from AI models as endpoints to agents as goal-directed system-level abstractions, and the critical need for observability and governance to manage agent behavior. Ultimately, designing for AgentEx is crucial for businesses to ensure high agent success rates, reduce errors, and foster safe, predictable automation, leading to a new "agentic economy"where AI agents autonomously produce value and drive business outcomes.

In this Episode, we will explore the concept of the Agentic Co-Intelligence. A New chapter in Human and AI collaboration. In an era where AI agents are building software, making decisions, and redefining how work gets done — how do humans work with AI agents? The answer is Agentic Co-Intelligence. Agentic Co-Intelligence is the idea that humans and agents must evolve together — as orchestrators, trainers, validators, and high-context collaborators. To work with agents, humans need a new literacy. Read more here

Agentic DevOps for the Rest of Us: A New Era of Intelligent SDLC. This episode elaborate concept of Agentic DevOps for the rest of the us (not just Microsoft) to enhance the software development life cycle using intelligent agents. The blog post published here on this topic. Learn how to use Agentic DevOps with tools like DSPy, Model Context Protocols and apply to GitHub PR Review, QA, SRE etc. Subscribe to The Superagentic AI Show for more episodes like this

This is an AI generated first episode introducing the The Superagentic AI Show and what kinds of content we are going to cover as part of this show. We will be having more real episodes coming soon to get you started into the Agentic AI journey. For more details, checkout super-agentic.ai