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Application monitoring tells you whether the service is healthy. Agent observability tells you whether the agent did the right thing — and lets you reconstruct how it decided. Teams running agents need both, and the gap between them is where agent incidents hide.

Most agents today authenticate as something else — a developer's token or a shared service account. Dedicated agent identity costs more to set up and pays for itself the first time you need to know which agent did what, or need to revoke one agent without breaking five.

Pre-deployment review checks what an agent is intended to do; runtime governance bounds what it can actually do. For deterministic software, review at the gate was mostly enough. Agents drift from their reviewed behaviour with every model update, which is why review-only governance keeps being surprised.

Role-based access control assigns agents fixed permission bundles; attribute-based access control evaluates each action against attributes of the agent, the resource, and the context. Agents strain RBAC faster than human users do, because an agent's safe permission set changes with its task, its confidence, and its risk profile.

Generative AI produces content for a person to use; agentic AI uses a model to decide and act — calling tools, writing to systems, working a goal across multiple steps. The two get confused because every agentic system has a generative model inside it. The difference that matters is what happens to the model's output: review by a human, or execution against your systems.

An AI agent is a thing — a deployed system that uses a model to act. Agentic is a quality — how much of the deciding and acting happens without a human in the loop. The terms get used interchangeably, and mostly that is harmless; the trap is governing by label when two systems called "agents" can sit at opposite ends of the autonomy dial.

Traditional automation executes rules someone wrote in advance; agentic AI pursues goals and decides the steps itself. That makes them suited to opposite kinds of work — and gives them opposite failure modes: automation breaks loudly when reality leaves the script, while an agent fails plausibly, producing confident wrong actions that nothing flags.

MCP is not a rival to your APIs — it is a layer that makes them usable by AI models. A REST API assumes a developer reading documentation; an MCP server assumes a model reading tool descriptions mid-task. The question is never which to build instead, but whether the consumers of a capability now include agents.

RAG gives a model access to knowledge at question time; fine-tuning changes the model's weights to alter how it behaves. Teams reach for them interchangeably because both 'teach the model about our stuff' — but they solve different problems, and the most common mistake is fine-tuning to inject facts, which is the job retrieval does better, cheaper, and reversibly.

LangChain is the broad toolkit — integrations, chains, and components for building LLM applications. LangGraph, from the same team, is the narrower engine for stateful agents: workflows modelled as graphs with explicit state, persistence, and human-approval stops. The confusion is natural because they share an ecosystem; the choice is about how much control your agent's loop needs.

A chatbot converses; an agent acts. Both may sit behind the same chat window and the same model, which is why the words blur — but a chatbot's output is a message for a human to act on, while an agent's output is the action itself: tool calls, records written, work completed. The window dressing is shared; the operational stakes are not.

Prompt engineering crafts the instructions you write to a model; context engineering manages everything the model sees — instructions, tool descriptions, retrieved documents, conversation history, and the budget that forces trade-offs between them. One is a writing skill; the other is an information-architecture discipline, and agents made the second one mandatory.

Both build multi-step agent systems; they disagree about the mental model. LangGraph hands you a graph — nodes, edges, explicit state — and makes you draw the control flow. CrewAI hands you a metaphor — agents as role-playing crew members collaborating on tasks — and infers the flow from the casting. Explicit control versus expressive abstraction is the whole choice.