From Fragile to Production-Ready: Reliable LLM Agents with Bedrock + Handit

LLM agents look great in demos. They plan, reason, call tools, and generate answers that feel almost magical. But put the same agent into production — and reality hits hard.

• Tool calls silently fail.




• Retrieval drifts and the model hallucinates.




• The same input produces a different plan every run.

That’s the problem: most agents don’t break loudly, they break quietly — and by the time you notice, it’s your users who are frustrated.

Bedrock Gives You Models — Handit Gives You Reliability

AWS Bedrock is the perfect foundation: world-class models (Claude, Llama, Cohere, Titan) plus enterprise features like Guardrails, Knowledge Bases, and Agents. But models alone don’t guarantee reliability.

Reliability is a system property. You need observability, evaluation, and continuous improvement running in the loop.

That’s where Handit comes in. With just three lines of code, it:

• Traces every step of your agent.




• Evaluates outputs against your rules.




• Improves prompts and settings automatically.




• Alerts when things drift.

The result: Bedrock-powered agents that stay reliable when real users depend on them.

The Reliability Loop (with Handit)

Reliability isn’t about the model alone — it’s about what happens every time your LLM is called. Handit adds a loop around those calls that makes your Bedrock agents stronger the more they’re used.

Here’s how the loop works:

1. Trace
   
   
   Every Bedrock LLM call is captured: inputs, outputs, tokens, latency. Nothing is hidden.




2. Evaluate
   
   
   Each output is tested against your rules — accuracy, grounding, clarity, compliance, latency — with custom evaluators that reflect your business needs.




3. Alert
   
   
   If accuracy drifts or latency spikes, you don’t wait for a user complaint — you get an alert right away.




4. Improve
   
   
   When issues are detected, Handit suggests prompt and parameter fixes automatically. You can apply them in the dashboard or let Handit ship a PR.

3-Line Integration on Agents Using Bedrock

Handit doesn’t replace your agent or Bedrock, it wraps your agent function. You keep your logic exactly the same, Handit just traces the entry point and runs the reliability loop around it.

Here’s what it looks like in practice:

# Example: agent logic calling Bedrock



import boto3, json



bedrock = boto3.client("bedrock-runtime", region_name="us-east-1")



async def classify_intent(user_message: str):

    body = {

        "anthropic_version": "bedrock-2023-05-31",

        "messages": [{"role": "user", "content": user_message}],

        "max_tokens": 256,

        "temperature": 0.0

    }

    resp = bedrock.invoke_model(

        modelId="anthropic.claude-3-5-sonnet-20240620-v1:0",

        body=json.dumps(body)

    )

    return json.loads(resp["body"].read())["output_text"]





async def search_knowledge(intent: str):

    # This could call Bedrock Knowledge Bases or a retrieval-augmented model

    return f"retrieved context for intent: {intent}"





async def generate_response(context: str):

    body = {

        "anthropic_version": "bedrock-2023-05-31",

        "messages": [{"role": "user", "content": context}],

        "max_tokens": 512,

        "temperature": 0.2

    }

    resp = bedrock.invoke_model(

        modelId="anthropic.claude-3-5-sonnet-20240620-v1:0",

        body=json.dumps(body)

    )

    return json.loads(resp["body"].read())["output_text"]

Then you wrap all of this in Handit’s tracing decorator at the entry point of the agent:

from handit_ai import tracing, configure

import os



configure(HANDIT_API_KEY=os.getenv("HANDIT_API_KEY"))



@tracing(agent="customer-service-agent")

async def process_customer_request(user_message: str):

    intent = await classify_intent(user_message)       # Bedrock call

    context = await search_knowledge(intent)           # Bedrock or KB call

    response = await generate_response(context)        # Bedrock call

    return response

That’s it:

• No refactor. Your agent logic (intent classification, retrieval, response generation) stays the same.




• Every call is traced. Handit logs inputs, outputs, latency, tokens.




• Evals run automatically. Accuracy, grounding, tone, or your custom rules.




• Fixes are suggested. Handit proposes prompt/parameter improvements and can even open a PR.

With just three lines, every request your agent makes to Bedrock is now part of the reliability loop.

What to Expect When You Run Your Agent

Once you add Handit to your Bedrock-powered agent, every run — whether in development or production — goes through the same reliability loop. Here’s what you’ll see:

• 
  Full visibility: every input, output, latency, and token count is captured as a trace. No more guessing what happened inside your agent.

• 
  Automatic evaluations: each response is checked against your chosen rules — accuracy, grounding, stability, policy, tone, or custom rubrics you define.

• 
  Continuous improvement: Handit doesn’t stop at detection — it generates fixes. You’ll see suggested prompt or parameter updates, which you can apply in the dashboard or merge directly via GitHub PRs.

With just a few lines of setup, your Bedrock agents don’t have to run blind anymore. Every call is traced, evaluated, and improved automatically — giving you the confidence that what works in a demo will keep working in production.

If you haven’t already, sign up at Handit.ai and add a new teammate to your workflow — one that monitors your agents, suggests fixes, and keeps reliability on autopilot so you can focus on building.

I’d love to hear what you’re building with Bedrock and how Handit fits into your workflow. Feel free to connect with me:

💼 LinkedIn


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