Caching and result reuse in Agentic AI - ML Experiment: Train & Evaluate

Experiment - Caching and result reuse

Problem:You have an AI agent that performs repeated expensive computations on the same inputs during training and inference.

Current Metrics:Computation time per query: 5 seconds; Model throughput: 10 queries per minute.

Issue:The AI agent is slow because it recomputes results for inputs it has already processed, wasting time and resources.

Your Task

Implement caching to store and reuse results of previous computations to reduce computation time by at least 50% without changing model accuracy.

Do not change the core AI model architecture or training data.

Caching must be implemented within the agent's inference pipeline.

Ensure cache consistency and correctness.

Hint 1

Hint 2

Hint 3

Solution

Agentic AI

import time

class AgentWithCache:
    def __init__(self, model):
        self.model = model
        self.cache = {}

    def compute(self, input_data):
        key = str(input_data)  # Simple key for caching
        if key in self.cache:
            return self.cache[key]
        # Simulate expensive computation
        result = self.model(input_data)
        self.cache[key] = result
        return result

# Dummy model function simulating expensive computation

def dummy_model(x):
    time.sleep(5)  # Simulate delay
    return x * 2

agent = AgentWithCache(dummy_model)

# First call with input 10 (slow)
start = time.time()
output1 = agent.compute(10)
end = time.time()
first_call_time = end - start

# Second call with input 10 (fast, cached)
start = time.time()
output2 = agent.compute(10)
end = time.time()
second_call_time = end - start

print(f"First call output: {output1}, time: {first_call_time:.2f} seconds")
print(f"Second call output: {output2}, time: {second_call_time:.2f} seconds")

Added a cache dictionary to store input-output pairs.

Modified compute method to check cache before running model.

Returned cached result immediately if available to save time.

Results Interpretation

Before caching: Each query took about 5 seconds, limiting throughput to 10 queries per minute.

After caching: Repeated queries took about 0.01 seconds, increasing throughput to 50 queries per minute.

Caching stores results of expensive computations to reuse later, greatly improving speed without affecting accuracy.

Bonus Experiment

Try implementing a cache with a size limit and eviction policy (like Least Recently Used) to manage memory.

💡 Hint

Use collections.OrderedDict or functools.lru_cache decorator to limit cache size and automatically remove old entries.

Practice

1.

What is the main benefit of caching in AI tasks?

easy

A. It saves time by reusing previous results.

B. It increases the size of the dataset.

C. It makes the model more complex.

D. It reduces the accuracy of predictions.

Solution

1. Step 1: Understand caching purpose

   Caching stores results from previous computations to avoid repeating the same work.

2. Step 2: Identify the benefit

   Reusing cached results saves time and speeds up AI tasks.

3. Final Answer:

   It saves time by reusing previous results. -> Option A

4. Quick Check:

   Caching benefit = Saves time [OK]

Hint: Caching means saving results to avoid repeat work [OK]

Common Mistakes:

• Thinking caching increases dataset size
• Believing caching reduces accuracy
• Confusing caching with model complexity

2.

Which Python code snippet correctly checks if a result is cached before computing?

cache = {}
key = 'input1'
# What to do next?

easy

A. if cache.has_key(key):
result = cache[key]
else:
result = compute()
cache[key] = result

B. if key in cache:
result = cache[key]
else:
result = compute()
cache[key] = result

C. if key not in cache:
result = cache[key]
else:
result = compute()
cache[key] = result

D. if cache[key]:
result = cache[key]
else:
result = compute()
cache[key] = result

Solution

1. Step 1: Check Python dictionary membership

   Use 'if key in cache' to check if key exists in dictionary.

2. Step 2: Use correct syntax to assign or compute

   If key exists, get cached result; else compute and save it.

3. Final Answer:

   if key in cache: result = cache[key] else: result = compute() cache[key] = result -> Option B

4. Quick Check:

   Python dict membership uses 'in' keyword [OK]

Hint: Use 'if key in dict' to check cache presence [OK]

Common Mistakes:

• Using deprecated has_key() method
• Checking 'if cache[key]' without key check
• Reversing condition logic

3.

What will be the output of this code?

cache = {}
def compute(x):
    print(f"Computing {x}")
    return x * 2

inputs = [1, 2, 1]
results = []
for i in inputs:
    if i in cache:
        results.append(cache[i])
    else:
        val = compute(i)
        cache[i] = val
        results.append(val)
print(results)

medium

A. [1, 2, 1]

B. [2, 4, 4]

C. [2, 2, 2]

D. [2, 4, 2]

Solution

1. Step 1: Trace the loop and caching behavior

   For input 1: not cached, compute(1)=2, cache[1]=2, results=[2]. For input 2: not cached, compute(2)=4, cache[2]=4, results=[2, 4]. For input 1 again: cached, results append cache[1]=2, results=[2, 4, 2].

2. Step 2: Confirm final results list

   The final printed list is [2, 4, 2].

3. Final Answer:

   [2, 4, 2] -> Option D

4. Quick Check:

   Cache reuse returns previous result [OK]

Hint: Cached inputs skip compute, reuse stored value [OK]

Common Mistakes:

• Assuming compute runs for repeated input
• Mixing up cached values
• Ignoring print output side effect

4.

Find the error in this caching code and select the fix:

cache = {}
def get_result(x):
    if x in cache:
        return cache[x]
    result = compute(x)
    return result

medium

A. Remove the cache dictionary entirely.

B. Change 'if x in cache' to 'if x not in cache'.

C. Add 'cache[x] = result' before returning result.

D. Return 'cache[x]' without checking if key exists.

Solution

1. Step 1: Identify missing cache update

   The function returns computed result but never saves it to cache, so caching fails.

2. Step 2: Fix by saving result in cache

   Insert 'cache[x] = result' before returning to store computed value.

3. Final Answer:

   Add 'cache[x] = result' before returning result. -> Option C

4. Quick Check:

   Cache must store new results [OK]

Hint: Always save new results to cache before returning [OK]

Common Mistakes:

• Forgetting to update cache after compute
• Reversing cache check condition
• Ignoring cache and recomputing every time

5.

You want to speed up an AI agent that processes user queries by caching results. Which strategy best balances memory use and speed?

• A. Cache all results forever.
• B. Cache only recent results with a size limit.
• C. Never cache, always compute fresh results.
• D. Cache results but never check before computing.

hard

A. Cache only recent results with a size limit.

B. Cache all results forever.

C. Never cache, always compute fresh results.

D. Cache results but never check before computing.

Solution

1. Step 1: Understand caching trade-offs

   Caching all results forever uses unlimited memory, which is impractical.

2. Step 2: Choose a balanced caching strategy

   Limiting cache size to recent results saves memory and keeps speed benefits.

3. Final Answer:

   Cache only recent results with a size limit. -> Option A

4. Quick Check:

   Limited cache balances memory and speed [OK]

Hint: Limit cache size to keep memory use reasonable [OK]

Common Mistakes:

• Caching everything without limit
• Not checking cache before computing
• Skipping caching entirely