Walking into the Midway in San Francisco for LangChain's Interrupt conference, the first thing I noticed wasn't the technology everyone was discussing, but who was doing the discussing. The demographic composition was notable: predominantly North Americans and Europeans, with significant representation from Asians—particularly developers of Indian and East Asian origin. Over the two days of the event, I managed to chat or identify just eleven Latin Americans among nearly a thousand attendees. I estimate about 12% were women, a minority also reflected in the speaker panels, while most identified as AI developers, CTOs, CEOs, or tech startup founders.

This homogeneity isn't just a sociological curiosity—it matters because these are the people “defining” how AI agents will interact with the rest of the world.

When AI Stops Being Just Conversation
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What exactly makes something an "agent" and not just another AI application? The difference is fundamental and worth explaining clearly. When you use ChatGPT or similar applications, you're essentially having a very sophisticated conversation with an intelligent calculator. You give it input, it processes the information, gives you output, and the only context is the conversation itself. It's impressive, but fundamentally reactive and what the development world calls "monolithic"—the entire process happens in a single closed block.

An AI agent represents a different conceptual leap. It's the difference between having an assistant who can only answer questions when you ask them directly, versus having a collaborator who can take initiative, plan, access memory or context, use tools, and execute concrete actions.

Imagine you're researching renewable energy solutions for rural communities. With a traditional monolithic application, you'd upload some academic papers, ask specific questions, and receive answers based only on that information; end of process. An AI agent would approach this completely differently. It could start by searching for current research on the topic, then identify which approaches have been successful in similar contexts, connect to databases of implemented projects, analyze relevant climate and geographic factors, and update or share information. All of this would happen in a coordinated way, with the agent maintaining the research thread, adapting to needs and tools to establish a complete workflow that involves humans in the exploratory or creative process.
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The Five Pillars of Agentic Systems
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During the first day's technical sessions, it became clear that effective agents need five components working in harmony.

First is orchestration—the ability to coordinate multiple steps toward a goal. An agent analyzing data doesn't just generate a report. It first decides what data it needs, then orchestrates a sequence: connect to the database, clean the information, identify patterns, cross-reference with external data if necessary, and maybe even decide what visualizations would be most useful for your specific context. This ability to plan and execute complex sequences is what separates an agent from a reactive tool.

Memory is the second critical component, and there's an important distinction here. Agents need both short-term and long-term memory. Short-term memory maintains the thread of the current conversation or task state—like when you remember what you were talking about with a colleague five minutes ago. Long-term memory is deeper: patterns it has learned, preferences you've expressed in the past, previous decisions that have worked well or poorly. Without memory, every interaction with an agent is like starting from scratch with a very intelligent amnesiac.

Tools represent the third element—the agent's ability to interact with the "outside world." This goes far beyond searching for information on the internet. An agent can connect to APIs, execute code, manipulate databases, send emails, create documents, or even control other software. I had the opportunity to speak directly with LangChain's development team, particularly about MCP (Model Context Protocol) and A2A (Agent-to-Agent) protocols, something that wasn't explored in depth during the talks. These protocols basically represent a type of tool that extends the agent's possibilities. These standards will allow agents to share context and information between different systems or apps fluidly, creating interconnected ecosystems that go far beyond today's isolated tools.

Human-in-the-loop—the ability to pause autonomous execution to request human input when necessary. This concept is so fundamental to LangChain that they literally named their conference "Interrupt" in reference to the LangGraph's interrupt( ) function that enables this interaction. An agent can be processing a complex task, recognize that it needs human clarification or approval, pause its execution, ask the necessary question, and then continue with the received response. It's the difference between blind automation and intelligent collaboration.

Finally, there's observability and evaluation—something that sounds technical but is fundamentally about trust. If an agent is going to make autonomous decisions, you need to understand why it made certain decisions, how reliable its conclusions are, and when something is going wrong. It's the difference between a high-variability black box versus a controlled and transparent system about its progress and reasoning.

The Gap Between Technical Capability and Human Reality
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This is where my conversations with other attendees took a more critical turn. While developers were excited discussing increasingly impressive technical capabilities, I noticed that conversations about human-machine interaction design need greater prominence in agent development.

Generative interfaces and multimodality are redefining how we interact with these systems in such a recent way that we're just beginning to understand their implications. The technical capabilities exist—LangGraph handles pauses, supervision, and coordination of parallel tasks—but the real challenge is how to design effective user experiences that leverage these new interaction modalities in diverse contexts.

When you can speak, show, or even upload files to communicate with an agent, how do we design interfaces that feel natural but maintain necessary precision? How do we balance system autonomy with the human need for contextual control? These user-centered design challenges deserve as much research as processing advances. Generative AI is maturing as a technology, and agent implementation is a sign of this. It's important to redefine how humans and machines collaborate. In the second part of this blog, we'll discuss the most relevant use cases from the second day of conferences.‍‍