Article: Agentic AI Architecture: Current Challenges and Future Opportunities

Agentic AI Architecture represents a new software architecture paradigm where artificial intelligence systems play a substantial role, with the potential to dramatically elevate productivity and end-user functionality in next-generation software systems. At the core of this architecture are agentic AI systems, which utilize generative AI (and other models) to optimize the use of AI capabilities, building upon the foundations of microservices and cloud-native architectures.

Agentic AI is an evolution of previous AI systems, transitioning from single-agent solutions to more complex chains, graphs, and multi-agent systems (MAS), where multiple agents collaborate to achieve specific goals. This development is ongoing, and further innovative use cases will likely emerge in the future. The choice of collaboration strategy or topology depends on the specific use case, with simpler solutions often being preferable in many scenarios.

The essence of agentic AI lies in its ability to leverage large language models (LLMs) by providing the right context, allowing agents to make the most out of the LLMs they interact with. Initially, predefined prompts were used, but the solutions gradually evolved to a dynamic context based on the output from other agents, context propagation, or data fetched from external sources using retrieval-augmented generation (RAG).

As agents accumulate context from various sources, they may encounter diminished reasoning capabilities due to the overwhelming amount of information. To address this challenge, engineers have developed context engineering, a separate practice that deals with managing context more effectively. This practice includes techniques such as progressive context disclosure, which allows agents to selectively load detailed information into the context window based on the user's prompt, ensuring the context used maximizes the model's likelihood of producing the desired outcome and minimizing the chance of hallucinations due to vague or inadequate context.

Context management, storage, optimization, and reuse are essential aspects of context engineering, often involving memory systems for AI agents that organize long-term memory (episodic, semantic, procedural) for storage and querying. This enables agents to supplement the context with past experiences, decisions, relevant facts, definitions, rules, and learned behaviors.

As agentic AI systems become increasingly integrated into larger enterprise architectures, standardized communications protocols for these agents have emerged to improve interoperability and ensure a seamless integration into existing systems. These protocols enable agentic AI systems to interact directly with various data sources, such as databases and services/APIs, further enhancing their capabilities and value within the enterprise setting.