AI agents feel new because modern language models made them visible. The idea is older: software that observes a state, chooses an action, and tries to move a task forward.
The difference in 2026 is that agents can read instructions, call tools, revise plans, and operate inside real workflows. That makes them powerful, but also harder to evaluate.
01 / OriginsEarly rule-based systems
Early AI systems were mostly symbolic. They followed explicit rules, searched state spaces, or used hand-built knowledge bases. They could look intelligent inside narrow boundaries, but they broke quickly outside those boundaries.
02 / AssistantsFrom chatbots to assistants
Chatbots and voice assistants made interaction easier. They could answer questions, trigger simple actions, and connect to services. Most still depended on rigid intents and carefully prepared integrations.
- Rule systems were predictable but brittle.
- Classic assistants were useful but narrow.
- Modern LLM agents are flexible but need stronger guardrails.
- The hard part is evaluation, not the demo.
03 / ShiftThe LLM agent moment
Large language models changed the interface. Instead of mapping every user phrase to a fixed intent, an agent can interpret a goal, break it into steps, ask for missing context, and call tools such as search, code execution, databases, or design systems.
An impressive agent demo is easy. A reliable agent workflow is an engineering system.
Field note
04 / Hard partsWhy agents are hard
Agents fail in ordinary ways: stale context, weak tool contracts, missing permissions, unclear goals, hallucinated state, and no acceptance test. Good agents need small scopes, explicit tools, observable steps, and a human-readable audit trail.
Practical takeaway. Build agents like product features. Define the job, inputs, tools, limits, success criteria, fallbacks, and review path before adding autonomy.
