AI Agent Core Technology Explained – Chapter 01: What Is a Foundational Agent?

What Makes an AI Agent Different from a Plain LLM

Without an agent framework, an LLM is merely a stateless function that takes text and returns text. It suffers from four fundamental limits: no state, no ability to act, knowledge cutoff, and single‑step reasoning. Real‑world tasks such as debugging code require multiple steps—reading files, running tests, observing errors, fixing, and re‑verifying— which a single LLM call cannot handle.

Technical Essence of an Agent

The solution is simple: embed the LLM in a loop and equip it with tools. This "Think → Act → Observe" cycle originates from the 2022 ReAct paper, which underpins most modern agent frameworks.

Thought : the model generates reasoning text for the current step.

Action : the model outputs a structured tool call (name + arguments).

Observation : the tool’s result is fed back into the next prompt.

Other paradigms also exist, such as Plan‑and‑Execute (global plan then stepwise execution), Reflexion (self‑reflection with memory after failures), and Tree/Graph of Thoughts (explicit reasoning trees with pruning and backtracking).

The core formula can be expressed as:

Agent = LLM + Tools + Memory + Loop

Later formulations simplify this to Agent = LLM + harness , where the LLM handles reasoning and decision‑making, tools provide actions, memory carries state across loops, and the loop enables multi‑step tasks.

Evolution Timeline

Early 2022 – Chain‑of‑Thought paper shows multi‑step reasoning improves accuracy.

Late 2022 – ReAct paper combines reasoning chains with tool calls, creating the modern agent loop.

2023 – OpenAI introduces Function Calling, making agents engineering‑ready.

2023 – AutoGPT popularity reveals issues like infinite loops, hallucinations, and token blow‑up, prompting more structured frameworks.

2024 – Projects such as LangGraph, Devin, Claude Code use state graphs and stronger models to bring agents to production.

Why Agents Are More Than Enhanced Workflows

Traditional workflow systems (e.g., n8n, Dify) have paths fixed at design time; the human decides the next step. Agents let the LLM infer the next path at runtime, handle unknown situations, recover from errors by adjusting reasoning, and trade predictability for flexibility. Consequently, agents excel where workflows struggle: fuzzy inputs, edge cases, and exception handling.

Product Perspective: A New Software Layer

From a product standpoint, agents are not a SaaS replacement but an additional layer atop existing stacks. Interaction shifts from users clicking UI elements to delegating tasks to an agent, which then organizes context, plans, and executes autonomously while humans review outcomes.

Coding Agents – The First Mature Scenario

Coding agents (e.g., Cursor, Claude Code, Codex) thrive because code environments naturally provide the four pillars an agent needs: immediate feedback via test pass/fail, precise structured context, stable tool interfaces (terminal, file system), and easy rollback with git. Replicating such feedback loops in other domains is essential for broader agent success.

Current Maturity Stage

Agents are still in an early, unstable phase—comparable to the Excel era of SaaS—where many products exist but vertical specialization is limited. The evolution path proceeds from chat‑based interfaces (lowest UI barrier) to Retrieval‑Augmented Generation, deep research automation, structured workflows, and finally AI‑driven coding.

Three Typical Agent Categories

Productivity‑Tool Agents : AI coding, documentation, research; focus on personal/team efficiency and long‑task execution.

Business‑Workflow Agents : sales, customer service, operations; emphasize business know‑how, system integration, permissions, audit, and human confirmation.

Multimodal AIGC Agents : image, video, marketing content generation; prioritize multimodel orchestration, asset pipelines, style control, and batch production.

Key metrics differ per category—retention and task completion for productivity agents; processing time, error rate, and cost for workflow agents; generation quality and adoption for AIGC agents. The overarching evolution direction is tool efficiency → process takeover → content creation.

Key Insight

Business‑workflow agents do not merely replace workflows; they fill the gaps that static workflows cannot address—handling ambiguous inputs, boundary conditions, and exceptions.

Recommended Reading

The foundational paper for all modern agents is ReAct: Synergizing Reasoning and Acting in Language Models (Yao et al., 2022), a concise six‑page work that outlines the engineering implementation behind today’s agent frameworks.