### Beyond Next-Token Prediction: The Architectural Shift to Agentic AI
We are living through a period of profound advancement in artificial intelligence, driven largely by the incredible capabilities of Large Language Models (LLMs). Models like GPT-4, Claude 3, and Llama 3 have demonstrated a remarkable fluency and breadth of knowledge that often feels like genuine intelligence. Yet, as practitioners in the field, it’s crucial to look past the impressive demos and understand the fundamental limitations of the underlying technology.
At their core, today’s LLMs are sophisticated next-token predictors. Given a sequence of text (the context), they are statistically optimized to predict the most probable next word, and the next, and the next. This is a monumental feat of engineering, but it’s not reasoning. An LLM, in its raw form, is a stateless function: it has no memory beyond its context window, no ability to act on the world, and no persistent goals. It is, in many ways, the world’s most advanced stochastic parrot.
The truly revolutionary work happening now isn’t just about building bigger models. It’s about changing the architectural pattern of how we *use* them. We are moving from the “LLM as a chatbot” paradigm to the “LLM as a reasoning engine” within a larger, stateful system. This is the shift to **agentic AI**.
—
### The Anatomy of an Agentic System
An agentic architecture reframes the LLM’s role. Instead of being the final interface for a user, the LLM becomes a central component—a “brain”—in a cyclical process of planning, acting, and observing. This system is composed of several key parts:
* **1. The Planning Module (LLM):** The process begins with a high-level goal from a user (e.g., “Summarize the key findings of the top 3 scientific papers on quantum computing published this week and email them to my team.”). The LLM’s first job isn’t to answer, but to *plan*. It breaks the complex goal down into a sequence of smaller, actionable steps.
“`yaml
plan:
– step: 1
action: search_arxiv
query: “quantum computing”
params: {date: “last_7_days”, sort: “relevance”}
– step: 2
action: analyze_results
input: [paper_1, paper_2, paper_3]
task: “Identify key findings and methodology for each.”
– step: 3
action: synthesize_summary
input: [analysis_1, analysis_2, analysis_3]
task: “Create a consolidated, executive-level summary.”
– step: 4
action: send_email
recipients: “team@example.com”
subject: “Weekly Quantum Computing Briefing”
body:
“`
* **2. Tool Use & Execution:** The agent has access to a set of external “tools,” which are simply APIs or functions. These could be anything from a web search API, a code interpreter, a database query function, or a corporate knowledge base. The system’s “executor” takes the LLM’s planned action (e.g., `search_arxiv`) and runs it, capturing the output. This is where the model transcends its internal knowledge and interacts with live, external data. Frameworks like LangChain and LlamaIndex have been instrumental in standardizing this tool-use pattern.
* **3. State Management & Observation:** This is the critical piece that overcomes the LLM’s stateless nature. After a tool is executed, the result (the “observation”) is not just shown to the user. It is stored and appended to the agent’s working memory or “scratchpad.” This memory, which persists across multiple turns, is then fed back into the LLM’s context for the next step. The agent now “knows” what it has done and what the results were, allowing it to adjust its plan, correct errors, or proceed to the next logical action. This creates a loop: **Think (LLM Plan) -> Act (Tool) -> Observe (Result) -> Think (LLM Re-plan)**.
—
### The Challenges on the Horizon
This architectural shift is immensely powerful, but it also introduces new and complex challenges.
* **Reliability and Error Handling:** LLMs can still “hallucinate” a tool that doesn’t exist or generate improperly formatted API calls. Building robust validation, error correction, and retry mechanisms is a significant engineering effort.
* **Latency and Cost:** A single user query can trigger a chain of multiple LLM calls and tool executions, leading to higher latency and significantly increased operational costs compared to a simple chatbot interaction.
* **Security:** Giving an AI agent the ability to execute code or interact with external APIs is a massive security concern. Creating secure, sandboxed environments to prevent prompt injection attacks from triggering destructive actions is paramount.
### Conclusion
The future of applied AI is not just about more eloquent chatbots. It’s about building autonomous agents that can perform complex, multi-step tasks. By wrapping LLMs in an architectural framework that provides them with tools, state management, and an execution loop, we are fundamentally changing their nature. We are moving from using them as generators of text to leveraging them as engines for orchestration and reasoning. The magic isn’t in the model alone; it’s in the system we build around it.
This post is based on the original article at https://techcrunch.com/2025/09/19/crack-the-code-to-startup-traction-with-insights-from-chef-robotics-nea-and-iconiq-at-techcrunch-disrupt-2025/.
