Skip to main content

9.1.3 Agent Development History

Learning Objectives​

After completing this section, you will be able to:

  • Understand that Agent did not appear out of nowhere as a brand-new concept
  • Explain the evolutionary relationship between rule systems, workflows, and modern Agents
  • See why large models made Agents truly usable
  • Experience the differences between systems from different stages through a small example

If you want a broader AI timeline, read this page together with A.3 AI Development History: 15 Stages and Key Papers. This section focuses only on the Agent thread.

Before Agents, automation already existed​

Early automation was more like “fixed scripts”​

Before large models existed, many automation systems were already at work:

  • Scheduled tasks
  • Automatic form processing
  • Rule engines
  • RPA process bots

These systems are valuable, but they all share one trait:

The path is basically written in advance.

Rule-based bots are more like “acting strictly by the script”​

For example, a customer service rule bot might work like this:

  • If the user mentions “refund,” reply with the refund policy
  • If the user mentions “certificate,” reply with certificate instructions

It usually does not really plan, and it is not very flexible at changing tactics.

The workflow era: stronger than rules, but still relatively fixed​

Workflows are “composable fixed processes”​

Later, systems became a bit more complex, and we started to see:

  • Conditional branches
  • Multi-step chaining
  • Tool combination

For example:

  1. Identify the user’s intent
  2. Query the database
  3. Use a template to generate a reply

This is already stronger than pure rules, but in many cases it is still a “pre-designed path.”

Why are workflows still important today?​

Because they are:

  • Stable
  • Controllable
  • Easy to debug

So even though Agents are very popular today, many real-world projects still rely heavily on workflows.

Why has this workflow approach not become outdated even today?​

Because it satisfies the three most practical needs in engineering:

  • Stability
  • Controllability
  • Auditability

That is also why many beginners later realize:

  • A system that is “more like an Agent” is not always more valuable than a workflow

In many real scenarios, a workflow is not a leftover from an old era, but more like:

The foundation that is still very important in the Agent era.

Why was it hard to build a general-purpose Agent before large models?​

Because “understanding the task” itself is hard​

In the past, systems were good at:

  • Executing according to rules
  • Processing structured fields

But they were not good at:

  • Understanding open-ended natural language instructions
  • Deciding the next step in uncertain situations

So many systems could only “automate,” not really “agentify”​

They could get things done, but what they handled was usually:

  • Fixed, explicit, structured tasks

Not:

  • Ambiguous, context-dependent tasks that require dynamic judgment

What exactly did large models change?​

They made the bridge from “natural language -> executable actions” much stronger​

One of the biggest changes brought by large models is not just that they chat better, but that they are better at:

  • Understanding open-ended instructions
  • Generating structured output
  • Choosing tools
  • Organizing intermediate steps

This means systems can finally:

Stop hard-coding every step manually, and instead let the model help decide the next step.

This is why modern Agents exploded in popularity​

When large models gained:

  • Instruction following
  • Tool calling
  • Long context
  • Stronger planning ability

Agents truly moved from concept to practical use.

Why do many people see this as the moment the “threshold was crossed”?​

Because before large models, automation systems mostly could only:

  • Follow templates
  • Follow fixed structures

But large models significantly improved, for the first time:

  • Understanding open-ended instructions
  • Generating structured actions
  • Continuing a task under vague conditions

This gives many people a strong feeling:

The system is no longer just “doing things by following a process,” but is starting to look like something that can organize steps around a goal on its own.

A small “evolutionary” example​

Below, let’s use the same task to look at the flavor of three different stages.

Rule-based bot​

def rule_bot(query):
    if "refund" in query:
        return "Please check the refund policy."
    if "certificate" in query:
        return "Please check the certificate instructions."
    return "Sorry, I do not understand your question."

print(rule_bot("How do I get a refund"))
print(rule_bot("How do I get a certificate"))

Expected output:

Please check the refund policy.
Please check the certificate instructions.

Workflow system​

def workflow_bot(query):
    if "refund" in query:
        doc = "Refund policy: You can get a refund within 7 days if your learning progress is below 20%."
        return f"Based on the knowledge base: {doc}"
    if "certificate" in query:
        doc = "Certificate instructions: You can receive a certificate after completing the project and passing the test."
        return f"Based on the knowledge base: {doc}"
    return "No workflow node matched."

print(workflow_bot("How do I get a refund"))

Expected output:

Based on the knowledge base: Refund policy: You can get a refund within 7 days if your learning progress is below 20%.

Simplified Agent​

def tool_search_policy(keyword):
    docs = {
        "refund": "Refund policy: You can get a refund within 7 days if your learning progress is below 20%.",
        "certificate": "Certificate instructions: You can receive a certificate after completing the project and passing the test."
    }
    for k, v in docs.items():
        if k in keyword:
            return v
    return "No related policy found."

def simple_agent(query):
    steps = []
    steps.append("First, determine the question type")

    if "refund" in query or "certificate" in query:
        steps.append("Decide to call the policy retrieval tool")
        evidence = tool_search_policy(query)
        steps.append(f"Evidence obtained: {evidence}")
        answer = f"Based on the retrieved policy, here is the answer: {evidence}"
    else:
        steps.append("No suitable tool can be determined right now")
        answer = "Sorry, I still do not know how to handle this task."

    return steps, answer

steps, answer = simple_agent("If I have not learned much, can I get a refund?")
print(steps)
print(answer)

Expected output:

['First, determine the question type', 'Decide to call the policy retrieval tool', 'Evidence obtained: Refund policy: You can get a refund within 7 days if your learning progress is below 20%.']
Based on the retrieved policy, here is the answer: Refund policy: You can get a refund within 7 days if your learning progress is below 20%.

In this example, although the Agent is still simplified, it already reflects the structure of “judge -> choose a tool -> use the result.”

From the AutoGPT wave to today​

What did the early wave bring?​

Early on, people saw that large models could:

  • Write plans on their own
  • Call tools on their own
  • Execute in loops on their own

This led to many attempts at “fully automated Agents.”

Later, people became more rational​

In practice, people found that:

  • Completely free-form Agents are not necessarily stable
  • Multi-step systems can accumulate errors
  • Costs and latency can become very high

So the industry gradually moved toward a more mature direction:

  • Use workflows to constrain Agents
  • Use tool calling to improve stability
  • Use evaluation and observability to improve controllability

This is a process of “moving from excitement to engineering.”

Why is this history especially valuable for beginners?​

Because it helps you avoid a very common misunderstanding:

  • Thinking that an Agent is always better the more autonomous it is

But the real world often is not like that. Many times, what is truly valuable is:

  • Stability
  • Explainability
  • Recoverability
  • Replayability

So the most important thing to remember about the AutoGPT era is not the hype itself, but that it was like an open experiment for the entire industry:

Everyone first saw the possibility, and then reality pulled things back to engineering constraints.

What do Agents look like today?​

No longer “infinite autonomy,” but “limited autonomy”​

More mature Agent systems usually:

  • Define clear goal boundaries
  • Restrict available tools
  • Record intermediate states
  • Set timeouts and safety guardrails

They are more like “intelligent executors with workflow constraints”​

That is also why many teams today do not pursue the “most free” Agent, but instead focus more on:

  • Stability
  • Replayability
  • Auditability

Common beginner misconceptions​

Thinking Agent history started with ChatGPT​

No. ChatGPT and LLMs only brought Agents into a new stage.

Thinking old systems are all outdated​

Many rule systems and workflows are still the main force in industry today.

Thinking more autonomy always means more advanced​

In real engineering, controllability is often more important than “sounding smarter.”

Summary​

The most important takeaway from this section is:

Agent is not a brand-new species that appeared out of thin air, but a leap in capability for automation systems in the era of large models.

Understanding its history helps you judge more calmly: when you should use an Agent, and when a workflow is actually enough.

Exercises​

  1. Add another tool to simple_agent(), such as a “calculator.”
  2. Summarize the differences between rule-based bots, workflows, and Agents in your own words.
  3. Think about this: why do many teams still keep a large number of fixed workflow nodes in Agent projects?