LLM Usage Patterns: Chats, Workflows, and Agents in AI

1. What is a Chat?¶

Theoretical View¶

A chat primarily involves conversational interaction: the AI model responds directly to user inputs with natural language outputs. It focuses on understanding intent and generating coherent, context-aware text.

Chat is reactive: it awaits user input and replies.
Interaction is typically a single or multi-turn dialogue.
Behavior is driven by prompt engineering and context history management.

Practical Implementation¶

Usually implemented as a single LLM call wrapped with minimal conversational context management (history, token limits).
Operational Footprint: Low Latency and Low Cost per query.

Example: A customer support chatbot answering product FAQs.

2. What is a Workflow?¶

Theoretical View¶

Workflows use LLMs as components within a predefined, structured pipeline of AI and non-AI tasks.

Consists of sequential or conditional steps.
Each step solves a specific subtask, like classification, summarization, or data extraction.
The process is deterministic and scripted by human engineers.

Note on Determinism: While workflows may include conditional branching and error handling that appear dynamic, their decision logic is entirely scripted by human engineers. This means workflows are deterministic and lack genuine autonomy — they do not adapt or revise their sequence of steps based on situational understanding beyond predefined, hard-coded rules.

Practical Implementation¶

Implemented as Python scripts or orchestrated pipelines (e.g., using frameworks like LangChain Pipelines).
Requires management of inputs/outputs between steps, error checking, and optional fallback mechanisms.
Operational Footprint: Moderate Latency and Moderate Cost due to multiple sequential LLM calls.

Example: A resume screening system that extracts skills, scores relevance, then generates a summary report.

3. What is an Agent?¶

Theoretical View¶

Agents are autonomous AI systems that perceive environment inputs, plan actions, dynamically select and execute tasks, and adapt based on context and feedback.

The agent’s power comes from integrating LLM reasoning with external tool/API invocation. It has dynamic decision-making capabilities, choosing the next step based on the result (observation) of the previous step, enabling iterative self-correction and feedback loops.

In sophisticated agent architectures, the planner-executor model is crucial: the LLM (planner) generates the next high-level decision, and the executor system performs the specific action (tool/API call) and reports the observation back to the LLM for the next planning cycle. This enables true self-direction beyond scripted automation.

Practical Implementation¶

Implemented as complex, iterative software architectures integrating LLMs with external APIs, databases, and tools.
Requires robust control flow logic, state management, and tool parsing.
Operational Footprint: High Latency and High Cost due to the iterative nature of the decision loop, high token usage for history/reflection, and multiple sequential API calls.

Example: A virtual assistant that plans a trip by querying weather, dynamically booking flights based on availability, and updating the itinerary as new information arrives.

4. Key Differences Summary¶

Aspect	Chat	Workflow	Agent
Purpose	Human-like conversation	Structured multi-step tasks	Autonomous task planning & execution
Architecture	Single LLM interaction	Fixed pipeline of modular steps	Dynamic, iterative control loop with tools & APIs
Autonomy	Low; reactive	Medium; scripted process (deterministic)	High; adaptive and self-directed (non-deterministic sequence)
Complexity	Simple	Moderate	High
Operational Cost	Low	Moderate	High (Token/Latency)

5. Practical Tips for AI Engineers 🛠️¶

Start with chats if your need is straightforward conversational AI.
Use workflows for automating well-defined multi-step AI tasks where the execution sequence is known and can be specified in advance.
Build agents when your system needs to autonomously plan, choose tools, and adapt its execution path based on intermediate results and complex context.
Operational Constraint: Remember that complexity scales with operational cost and latency. Agents are the most flexible but typically incur the highest latency and token consumption due to iterative self-reflection and decision-making calls.
Production Agents: Advanced agents designed for production require strong evaluation, guardrails (like sandboxed tool execution), and robust observability mechanisms, including human-in-the-loop checkpoints, to ensure safety, correctness, and compliance in autonomous decision-making.

6. Example Code Skeletons¶

Chat (Python + Prompt)¶

user_input = "What are the benefits of solar energy?"
prompt = f"Answer in a friendly tone: {user_input}"
response = llm.call(prompt)
print(response)

Workflow (Chained Steps)¶

def extract_topics(text):
    return llm.call(f"Extract main topics from: {text}")

def summarize(topics):
    return llm.call(f"Write a summary based on: {topics}")

business_question = "Explain renewable energy trends."
topics = extract_topics(business_question)
summary = summarize(topics)
print(summary)

Agent (Iterative Decision + Tool Invocation)¶

This architecture demonstrates the iterative nature where the LLM is called repeatedly to make the next decision based on the current state and observed results.

class Agent:
    def __init__(self, tools):
        self.tools = tools

    def act(self, user_input, max_steps=5):
        current_state = {'task': user_input, 'history': []}

        for step_count in range(max_steps):
            # 1. LLM plans the NEXT action based on the *current state*
            decision = llm.call(f"Based on history {current_state['history']}, what is the best next action for task: {user_input}?")
            
            tool_name, args = parse_decision(decision) # Hypothetical function to parse tool choice

            if tool_name == "FINISH":
                print("Task complete.")
                return current_state.get('result', "Success.")

            if tool_name in self.tools:
                # 2. Execute the action (Tool invocation)
                result = self.tools[tool_name](args)
                
                # 3. Update state with the observation/result for the next loop
                current_state['history'].append({'action': tool_name, 'observation': result})
            else:
                return f"Error: Invalid tool {tool_name} used."
        
        return "Max steps reached without finishing the task."

# Hypothetical usage
agent = Agent(tools={'weather_api': get_weather, 'email': send_email})
agent.act("Schedule outdoor event next week")