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.

Read this image from bottom to top: fixed scripts solve stable tasks, workflows add structure, tool-using LLM systems add judgment, and modern Agents begin to manage goals, tools, memory, and feedback loops together.

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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.

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Why has this workflow approach not become outdated even today?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

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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

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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.

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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.”

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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.

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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

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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.”

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Evidence to Keep

Keep this page’s proof of learning as a small evidence card:

Agent Boundary

how this differs from chatbot or fixed workflow

Goal State Action

goal, current state, next action, observation

Architecture Parts

planner, tools, memory, guardrails, evaluator

Failure Check

over-autonomy, vague goal, missing state, or no trace

Next Action

build the smallest traceable single-agent loop

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.

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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?

Project reference and review notes

1. A calculator tool should be routed only for numeric tasks, validate inputs, and return a structured result or error.
2. Rule bots match fixed conditions; workflows execute predefined steps; Agents choose tools and actions based on goal, context, and observations.
3. Fixed nodes remain useful because they are easier to test, audit, secure, and recover. Many real Agent systems mix deterministic workflow with flexible Agent steps.