Building AI agents with LangChain, or any agentic framework, starts innocently enough. You define a tool, give the agent access, perhaps wire in some prompts, and watch as it begins to reason and act. But then the complexity creeps in. Quickly, one realizes this is not software development in the classical sense. The metaphors of functions, classes, and control flow, which served faithfully for decades, begin to feel like the wrong map for this terrain.
Software engineering was about constructing machinery. The work of the programmer was precise mechanics: inputs and outputs, states and transitions. If the system behaved unexpectedly, it was always a traceable misalignment between the blueprint and the running code. But with agentic AI, we are not building mechanisms so much as cultivating dispositions. An agent is not a machine with fixed operations; it is a loosely contained reasoning process, guided by prompts, memories, and tool access. It does not execute code in the strict sense—it executes possibilities.
This is why agent development feels opaque when one insists on the mindset of classical programming. A developer asks, “Where’s the flowchart? Where is the deterministic path?” But the very idea of a fixed flow is missing the point. Agents do not flow; they emerge.
The competence required here is architectural, but not in the software architecture sense of layering components, defining APIs, or partitioning services. It is closer to enterprise architecture—the discipline of aligning complex, adaptive systems with organizational purpose. Enterprise architects are not primarily concerned with lines of code but with orchestrating capabilities, constraints, and interactions across a whole ecosystem. They ask questions of alignment and coherence: How does this system serve the broader objective? How do its parts coordinate without collapsing into chaos?
Agentic AI design is similar. You are not merely coding; you are aligning a reasoning process with intent. You are shaping boundary conditions, designing affordances, and orchestrating multiple modalities of action. The technical challenge is not, “How do I write this algorithm?” but “What capacities should this agent embody? What feedback loops ensure it improves? What guardrails keep it aligned?”
LangChain and its peers give us primitives: memory, tools, retrievers, reasoning loops. But the real art is not in wiring them mechanically—it is in shaping an architecture of thought. One must think like an enterprise architect mapping the flows of information, responsibilities, and decisions across a living organization. The agent, like an organization, is semi-autonomous: guided by high-level objectives, constrained by policies, equipped with resources, and always a little unpredictable.
So perhaps the right mindset for agentic AI is this: stop trying to program agents as though they were functions. Instead, design them as though they were institutions. Give them clarity of purpose, a structured environment, and a well-considered set of affordances. Accept that their behavior is emergent, and your role is less mechanic than gardener—cultivating, pruning, and shaping until something coherent takes form.
We are not just writing software anymore. We are designing agents the way architects design enterprises: as living systems whose essence lies not in their mechanics but in the alignment of purpose, structure, and possibility.