My Struggle: Getting AI Agents to Finish Tasks

Hey there, agent101.net fam! Emma here, back in your inbox (or browser, rather) with another dive into the wonderful, sometimes bewildering, world of AI agents. Can you believe it’s already April 2026? Feels like yesterday we were all trying to figure out if ChatGPT was going to take our jobs or just write our emails better. (Spoiler: it’s mostly the latter, for now!)

Today, I want to talk about something I’ve been wrestling with a lot lately: getting AI agents to actually finish a multi-step task without me holding their digital hand every single step of the way. We’ve all seen the flashy demos, right? “AI agent builds a website from a single prompt!” “AI agent designs a marketing campaign and writes all the copy!” Then you try it yourself, and it’s more like, “AI agent writes a really good first sentence, then gets stuck deciding on a font and asks for clarification 17 times.” Sound familiar?

My goal with agent101.net is to make this stuff accessible for beginners, and honestly, the gap between the hype and the reality can be a real motivation killer. So, today, we’re going to tackle a very specific, very practical problem: how to set up a simple AI agent with a multi-step goal, specifically focusing on building in some basic error handling and retry mechanisms so it doesn’t just give up at the first sign of trouble.

This isn’t about building the next AutoGPT from scratch. This is about understanding the core loops and logic that make these agents persistent, even when things don’t go perfectly. Because let’s be real, in the real world, things *never* go perfectly.

The Frustration of the Flailing Agent

Let me tell you about “Project Blossom.” A few weeks ago, I had this idea for a small side project: an agent that could scour recent tech news for mentions of specific AI agent frameworks (think CrewAI, LangChain, etc.), summarize the key developments, and then draft a short social media post about them. Seemed simple enough, right? A classic three-step process:

1. Search for news.
2. Summarize findings.
3. Draft social post.

I started with a very basic LangChain agent (because it’s what I’m most comfortable with for these kinds of experiments) and a few tools. My first attempts were… disheartening. If the search tool returned no relevant articles, the agent would just kind of… freeze. Or, even worse, it would try a null result, leading to gibberish. If the summarization step failed because the text was too long or too short, it would often just stop, declaring its task complete but having produced nothing useful.

It was like watching a toddler try to build a tower of blocks. One block falls, and the whole project is abandoned, often with a frustrated whimper (or, in the agent’s case, a Python traceback). I realized that for these agents to be truly useful, especially for beginners who aren’t going to babysit every single token, they need a bit more resilience baked in.

Beyond the Happy Path: Introducing Basic Retry Logic

The core idea here is simple: if a step fails, don’t just quit. Try again. Maybe with a slightly different approach, or after a short delay. This is a fundamental concept in software engineering, and it’s surprisingly effective when applied to AI agents.

For our practical example, let’s simplify Project Blossom a bit. We’ll focus on an agent whose job is to:

1. **Fetch a specific piece of information from an API.** (Let’s say, the current price of a stock, or the weather in a city.)
2. **Process that information.** (Maybe check if the price is above a certain threshold, or if it’s raining.)
3. **Report the outcome.**

The key failure point we’ll address is the API call. APIs can be flaky. Network issues, rate limits, invalid requests – all sorts of things can go wrong. Our agent shouldn’t just throw its hands up.

Tools We’ll Need (Concepts, Not Necessarily Specific Libraries)

• An LLM (like GPT-3.5 or Claude 3 Haiku) to act as our agent’s brain.
• A tool for making API requests.
• A way to define our agent’s steps and logic (LangChain is great for this, but the principles apply broadly).
• Basic Python knowledge.

Example Scenario: Checking a Stock Price

Let’s imagine we want an agent that checks the stock price for a given ticker. If it successfully gets the price, it reports it. If not, it tries a couple more times before giving up. We’ll simulate an unreliable API for demonstration.

Step 1: The (Potentially Unreliable) Tool

First, we need a tool that our agent can use. In a real scenario, this would be `requests.get()` to an actual stock API. For our example, let’s create a simple Python function that simulates an API call, failing randomly to demonstrate the retry mechanism.

import random
import time

def get_stock_price_simulated(ticker: str) -> str:
 """
 Simulates fetching a stock price. Fails randomly 50% of the time.
 """
 if random.random() < 0.5: # 50% chance of failure
 print(f"--- Simulated API call for {ticker} FAILED! ---")
 raise ConnectionError(f"Could not connect to stock API for {ticker}. Try again.")
 
 # Simulate a successful call
 price = round(random.uniform(100.0, 1000.0), 2)
 print(f"--- Simulated API call for {ticker} SUCCESS! Price: ${price} ---")
 return f"The current price of {ticker} is ${price}."

Notice that `raise ConnectionError`? That’s our agent’s “oops, something went wrong” moment. Without a retry mechanism, this would often halt the agent’s progress.

Step 2: Building the Agent Loop with Retry Logic

Now, how do we get our agent to use this tool and retry if it fails? This is where the agent’s “executor” or “run loop” comes in. Instead of just calling the tool once, we’ll wrap the tool call in a `try…except` block and add a loop.

For simplicity, I’m going to show this as a more programmatic loop rather than a full LangChain agent, to highlight the core retry logic. In LangChain, you’d integrate this into your custom tool definitions or your agent’s execution chain.

from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import PromptTemplate
import os

# Set your OpenAI API key (replace with your actual key or load from environment)
# os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY" 

# Let's wrap our simulated function in a LangChain tool
@tool
def fetch_stock_price(ticker: str) -> str:
 """
 Fetches the current stock price for a given ticker symbol.
 Includes retry logic.
 """
 max_retries = 3
 for attempt in range(max_retries):
 try:
 print(f"\nAttempt {attempt + 1} to fetch price for {ticker}...")
 result = get_stock_price_simulated(ticker)
 return result
 except ConnectionError as e:
 print(f"Error on attempt {attempt + 1}: {e}")
 if attempt < max_retries - 1:
 print(f"Retrying in 2 seconds...")
 time.sleep(2) # Wait a bit before retrying
 else:
 print(f"Max retries reached for {ticker}. Giving up.")
 return f"Failed to get stock price for {ticker} after {max_retries} attempts."
 return "An unexpected error occurred." # Should not be reached

# Now, let's define our agent's core components
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0) # Using a low temp for more deterministic behavior
tools = [fetch_stock_price]

# Define the prompt for the agent
prompt_template = PromptTemplate.from_template("""
You are an intelligent assistant whose sole purpose is to get the current stock price for a given ticker symbol.
You have access to the following tools:
{tools}
Use the fetch_stock_price tool to get the price.
After getting the price, report it clearly.

The user will provide a ticker symbol.

{agent_scratchpad}
""")

# Create the agent
agent = create_react_agent(llm, tools, prompt_template)

# Create the agent executor
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Run the agent
print("\n--- Running Agent for GOOG ---")
agent_executor.invoke({"input": "GOOG"})

print("\n--- Running Agent for MSFT ---")
agent_executor.invoke({"input": "MSFT"})

print("\n--- Running Agent for AAPL ---")
agent_executor.invoke({"input": "AAPL"})

Let's break down what's happening in the `fetch_stock_price` tool:

• `max_retries = 3`: We've set a limit. We don't want it to try forever.
• `for attempt in range(max_retries):`: This loop will try the operation up to `max_retries` times.
• `try...except ConnectionError as e:`: This is the magic. If `get_stock_price_simulated` raises a `ConnectionError` (which it does 50% of the time), the code inside the `except` block runs.
• `time.sleep(2)`: A small pause. This is crucial for real-world APIs to avoid hitting rate limits too quickly and to give the network a chance to recover.
• If all retries fail, it returns a clear error message instead of just crashing.

When you run this code, you'll see the agent attempting to call `fetch_stock_price`. Sometimes it will succeed on the first try. Other times, you'll see it print "Simulated API call FAILED!" and then "Retrying in 2 seconds..." before trying again. This is exactly what we want!

Why this matters for beginners:

This simple retry mechanism dramatically increases the perceived "intelligence" and robustness of your agent. For someone just starting out, seeing their agent gracefully handle a temporary failure instead of just breaking can be a huge confidence booster. It teaches a fundamental principle of building resilient systems, not just AI agents.

Beyond Simple Retries: Other Forms of Resilience

While basic retries are a fantastic starting point, there are other ways to build more robust agents:

1. Input Validation and Pre-checks

Before even attempting a tool call, can your agent check if the input it's about to provide is valid? For example, if it needs an email address, can it use a regex pattern to confirm it looks like an email before trying to send one? This prevents unnecessary tool calls and errors.

My experience: I once had an agent that was supposed to generate a UUID. Sometimes, it would output something slightly malformed. Instead of letting it pass that malformed UUID to the next tool (which would fail), I added a step to validate the UUID format. If invalid, the agent would simply try generating it again.

2. Fallback Tools or Strategies

If a primary tool fails even after retries, can the agent pivot to an alternative? For instance, if your primary stock API is down, maybe there's a secondary, less preferred one it can try. Or, if it can't find information with a web search, maybe it can formulate a slightly different query or even politely tell the user it can't complete the task with its current tools.

This is where agent design gets really interesting, as you're essentially building multiple "paths" for the agent to take depending on the outcome of previous steps.

3. Self-Correction Prompts

This is a bit more advanced but incredibly powerful. If an agent receives an error message from a tool (like "Invalid API key" or "Input too long"), can it be prompted to analyze that error message and propose a fix? For instance, if it gets "Input too long," it might try summarizing the text first before passing it to the tool again.

This requires giving the agent access to its own previous actions and the error outputs, along with a prompt that encourages reflective problem-solving. It's like having a mini-debugger inside your agent's brain.

Actionable Takeaways for Your Next Agent Project:

1. Identify Failure Points: When you design your agent's workflow, think about every step. Where could an API call fail? Where could an input be invalid? Where could the LLM generate a poor response?
2. Implement Basic Retries First: For any external tool calls (APIs, web scraping, file I/O), wrap them in a `try...except` block with a simple retry loop and a short `time.sleep()`. This is low-hanging fruit for robustness.
3. Add Input Validation: Before calling any tool, consider if you can add a small validation step to ensure the inputs are in the expected format. This can save a lot of headaches down the line.
4. Keep Error Messages Clear: When an agent does fail (or when a tool returns an error), make sure the message it reports is helpful. "Failed to get X because Y" is much better than "An error occurred."
5. Start Small, Iterate: Don't try to build a perfectly resilient super-agent on your first try. Start with a basic agent, identify where it breaks, and then add specific resilience mechanisms to address those breakages.

Building resilient AI agents isn't about writing more complex code; it's about anticipating failure and guiding the agent on how to handle it. It's the difference between a frustrating, fragile script and a genuinely helpful, persistent assistant. And for us beginners, getting these foundational concepts right makes the whole journey a lot more fun and a lot less like banging your head against a wall.

Until next time, keep building, keep experimenting, and don't be afraid to break things (because that's how we learn to fix them!).

Cheers,

Emma Walsh

Blogger at agent101.net

🕒 Published: April 8, 2026