Handling retrieval failures gracefully in Agentic AI - Model Metrics & Evaluation

Metrics & Evaluation - Handling retrieval failures gracefully

Which metric matters for this concept and WHY

When handling retrieval failures gracefully, the key metric is Recall. Recall tells us how many of the relevant items the system successfully retrieved. If retrieval fails often, recall drops, meaning the system misses important information. High recall ensures the system finds most of what it needs, even if some attempts fail. Additionally, Failure Rate (percentage of retrieval attempts that fail) is important to track to understand how often the system cannot get data.

Confusion matrix or equivalent visualization (ASCII)

Retrieval Outcome Confusion Matrix (Simplified):

               | Retrieved Relevant | Retrieved Irrelevant |
---------------|--------------------|---------------------|
Relevant Items |         TP         |          FN         |
Irrelevant     |         FP         |          TN         |

Where:
- TP (True Positive): Relevant data retrieved successfully
- FN (False Negative): Relevant data not retrieved (failure)
- FP (False Positive): Irrelevant data retrieved
- TN (True Negative): Irrelevant data not retrieved

Total retrieval attempts = TP + FP + FN + TN

Failure Rate = FN / (TP + FN)  (how often relevant data retrieval fails)

Precision vs Recall tradeoff with concrete examples

In retrieval, Recall is about finding all the relevant data, while Precision is about how many retrieved items are actually relevant.

Example 1: Search engine
If the system retrieves many results but misses some relevant ones, recall is low. If it retrieves only a few but very accurate results, precision is high but recall may be low. For retrieval failures, recall matters more because missing important data is worse than extra irrelevant data.

Example 2: Medical diagnosis retrieval
Missing a relevant medical record (low recall) can be dangerous. So, the system should tolerate some irrelevant data (lower precision) to keep recall high and avoid retrieval failures.

What "good" vs "bad" metric values look like for this use case

• Good: Recall above 90%, Failure Rate below 10%. The system finds most relevant data and rarely fails to retrieve.
• Bad: Recall below 70%, Failure Rate above 30%. Many relevant items are missed, causing poor user experience or wrong decisions.
• Precision can be moderate (70-80%) if recall is high, since some irrelevant data is acceptable to avoid failures.

Metrics pitfalls

• Ignoring recall: Focusing only on precision can hide retrieval failures, as the system may retrieve few but very accurate items, missing many relevant ones.
• Accuracy paradox: High overall accuracy can be misleading if the dataset is imbalanced (many irrelevant items). The system might appear good but fail to retrieve relevant data.
• Data leakage: If retrieval uses future or test data accidentally, metrics look better but don't reflect real failures.
• Overfitting: The system may perform well on training data retrieval but fail in real scenarios, causing high failure rates.

Self-check question

Your retrieval system has 98% accuracy but only 12% recall on relevant data. Is it good for production? Why not?

Answer: No, it is not good. Despite high accuracy, the very low recall means the system misses most relevant data. This leads to many retrieval failures, which harms user trust and system usefulness. Improving recall is critical even if accuracy drops slightly.

Key Result

Recall and failure rate are key to measure retrieval success and handling failures gracefully.

Practice

1. Why is it important to handle retrieval failures gracefully in agentic AI systems?

easy

A. To keep the AI running smoothly without crashing

B. To make the AI run faster

C. To increase the size of the data retrieved

D. To avoid using any default values

Solution

1. Step 1: Understand retrieval failures

   Retrieval failures happen when the AI cannot get the needed data, which can cause errors.

2. Step 2: Importance of graceful handling

   Handling failures gracefully means preventing crashes and keeping the AI working by managing errors properly.

3. Final Answer:

   To keep the AI running smoothly without crashing -> Option A

4. Quick Check:

   Graceful failure handling = prevent crashes [OK]

Hint: Think about avoiding crashes by handling errors safely [OK]

Common Mistakes:

• Assuming failures speed up the AI
• Ignoring the need for default values
• Believing more data is always retrieved

2. Which Python syntax correctly handles a retrieval failure using try-except?

easy

A. try:
data = retrieve_info()
except Exception:
data = None

B. if data == None:
retrieve_info()
else:
pass

C. try:
data = retrieve_info()
finally:
data = None

D. data = retrieve_info() if data else None

Solution

1. Step 1: Identify try-except usage

   try: data = retrieve_info() except Exception: data = None uses try-except to catch errors during retrieval and sets data to None if an error occurs.

2. Step 2: Check other options for correctness

   Options A, B, and C misuse syntax or logic for error handling.

3. Final Answer:

   try: data = retrieve_info() except Exception: data = None -> Option A

4. Quick Check:

   try-except for errors = try: data = retrieve_info() except Exception: data = None [OK]

Hint: Look for try-except blocks catching exceptions [OK]

Common Mistakes:

• Using if without try-except for errors
• Misusing finally block to handle errors
• Incorrect conditional expressions

3. What will be the output of this code snippet?

def get_data():
    try:
        return None
    except:
        return 'Error'

result = get_data() or 'Default'
print(result)

medium

A. None

B. Default

C. Error

D. Exception

Solution

1. Step 1: Analyze get_data function

   The function returns None without raising an exception, so except block is skipped.

2. Step 2: Evaluate result assignment

   Since get_data() returns None (which is falsey), the expression uses 'Default' instead.

3. Final Answer:

   Default -> Option B

4. Quick Check:

   None or 'Default' = 'Default' [OK]

Hint: Remember None is falsey, so 'or' picks the default [OK]

Common Mistakes:

• Thinking None prints as 'None' string
• Assuming except block runs without error
• Confusing return values with exceptions

4. Identify the error in this code that tries to handle retrieval failure:

def fetch_data():
    try:
        data = retrieve()
    except:
        data = None
    return data

result = fetch_data()
print(result)

medium

A. Data variable is not defined

B. Missing parentheses in retrieve call

C. No return statement in function

D. No specific exception caught in except block

Solution

1. Step 1: Check function structure

   The function calls retrieve() correctly and returns data, so no missing parentheses or return issues.

2. Step 2: Analyze except block

   The except block catches all exceptions without specifying which, which is bad practice and can hide bugs.

3. Final Answer:

   No specific exception caught in except block -> Option D

4. Quick Check:

   Use specific exceptions, not bare except [OK]

Hint: Avoid bare except; specify exceptions to catch [OK]

Common Mistakes:

• Thinking missing parentheses cause error
• Ignoring importance of specific exceptions
• Assuming data is undefined

5. You want your AI agent to retrieve user info but return a safe default if retrieval fails. Which approach is best?

def get_user_info(user_id):
    try:
        info = retrieve_user(user_id)
        if info is None:
            return {'name': 'Guest', 'id': 0}
        return info
    except RetrievalError:
        return {'name': 'Guest', 'id': 0}

hard

A. Return None on failure and handle later

B. Raise error immediately without handling

C. Use try-except and return a default dict on failure or missing data

D. Return empty string on failure

Solution

1. Step 1: Understand retrieval and failure cases

   The function tries to get user info, checks if data is missing (None), and handles exceptions.

2. Step 2: Evaluate handling strategy

   Returning a default dictionary for missing or failed retrieval keeps AI stable and predictable.

3. Final Answer:

   Use try-except and return a default dict on failure or missing data -> Option C

4. Quick Check:

   Safe defaults on failure = Use try-except and return a default dict on failure or missing data [OK]

Hint: Return safe defaults inside try-except for smooth AI [OK]

Common Mistakes:

• Returning None and not handling later
• Raising errors without fallback
• Returning empty strings instead of structured defaults