Building a Simple Agent Workflow
A simple end-to-end workflow
The workflow
A basic agent workflow starts with a user request, interprets the goal, checks whether tools or memory are needed, chooses an action, observes the result, and repeats if necessary. This flow is the backbone of many practical systems.
A beginner agent pipeline
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Step 1: Receive the user goal.
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Step 2: Decide if the goal needs planning or a tool.
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Step 3: Execute one action.
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Step 4: Inspect the result.
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Step 5: Continue, revise, or stop.
Stop conditions matter
A good agent knows when to stop. Otherwise it may repeat itself, waste time, or create unnecessary actions.
Example
If the goal is "summarize this article," the agent may retrieve the article, extract key points, draft a summary, then compare the draft against the source for missing ideas.
What should an agent do after taking an action?
Agentic systems rely on feedback loops, so the result should guide the next step.
Correct answer: Observe the result and decide what to do next
Why is a stop condition important?
Stop conditions keep behavior bounded and efficient.
Correct answer: It prevents the agent from looping forever or wasting resources.
Workflow mindset
You can think of an agent as a small manager: it receives a task, delegates or performs work, checks progress, and knows when the job is complete.
BUILD YOUR FIRST WORKFLOW
Step-by-step: from idea to a working agent pipeline
The 6-Step Agent Workflow
Write a clear, scoped objective with success criteria and boundaries.
Select tools your agent needs: search, code runner, file reader, API client.
Define role, tone, rules, and hard constraints for your agent.
Execute a test case. Log every thought, action, and observation.
Did it meet the goal? Measure accuracy, efficiency, cost per run.
Refine system prompt, add error handling, improve tool descriptions. Repeat.
Minimal Agent Pseudocode
def run_agent(goal):
messages = [system_prompt, {"role":"user","content":goal}]
while True:
response = llm.complete(messages)
if response.is_final: return response.text
result = execute_tool(response.tool_call)
messages.append(result)