The Real History of AI Agents

"Agent" as a concept in software genuinely predates large language models by decades. This part covers that real, brief history — enough to understand why part 1's ReAct loop represented a genuine, meaningful advance, not a rebrand of an old idea.

Real, pre-LLM "agents": scripted automation

1990s-2000s: "software agents" often meant scripted, real automation
  — a script that checks a condition and takes a fixed, real,
  pre-programmed action, with no actual reasoning involved at all
Real, illustrative example: a scheduled real job that checks
  inventory nightly and sends an email if stock drops below a
  hardcoded real threshold

This genuinely qualifies as automation, but not as an "agent" under part 1's real, precise definition — there's no real reasoning step; the response to any given condition is entirely fixed in advance by whoever wrote the script.

Real, rule-based expert systems

Referenced in the AI Fundamentals series' own history coverage:
  1980s-1990s expert systems used hand-coded, real "if-then" rules
  to make decisions within a narrow, real domain

These systems could genuinely chain several real rules together — closer to part 1's loop structurally — but each individual real decision was still a lookup against an explicit, hand-written rule, not a genuine, flexible reasoning process adapting to a real situation the rule-writer never anticipated.

The real, genuine gap LLMs closed

Pre-LLM automation: rigid, real, pre-programmed responses to
  anticipated conditions
LLM-based agents (this series): genuine, flexible REASONING about a
  real, possibly novel situation, deciding what tool to use and why,
  without every possible real scenario being explicitly hand-coded
  in advance

This is the real, honest, meaningful advance — not that "agents" are a new idea, but that the reasoning step itself became genuinely flexible and general, rather than a fixed, hand-written rule table.

2022-2023: the real, early LLM agent experiments

Real, early public projects (widely discussed at the time) attempted
  fully autonomous, open-ended LLM agents — genuinely ambitious, but
  frequently ran into real, practical problems: looping
  unproductively, real hallucinated tool results, no real safety
  limits

This is directly why part 18 of the LLM & Advanced AI series covered guardrails as a genuine, necessary requirement, not an optional add-on — the real, early history of LLM agents includes genuine, documented failure modes from skipping exactly those safeguards.

Where real, current agent development actually stands

Real, current best practice (this series' own approach, starting
  part 6): narrower, real, well-scoped agents with explicit tools
  and real safety limits — genuinely more reliable than the fully
  open-ended, unconstrained experiments from the earlier era

This series builds real agents the way current, practical experience actually recommends — scoped to a specific, real task with a defined, real toolset, not an unconstrained, general-purpose system attempting anything.

Why this context matters before building anything

Knowing the real, honest failure modes of earlier, more ambitious
  agent attempts is directly why this series builds narrow,
  well-scoped real agents (part 6's inventory checker, part 11's
  research agent) before ever reaching for the added complexity of
  a real, multi-agent system (part 15)

Next: what function calling actually is in LLMs — the specific, real technical mechanism underneath every "ACT" step in part 1's loop.