📈 The Problem: The Developer Time Drain

Ever wonder how much time do we developers spend debugging, monitoring, and resolving issues, time that could be better spent building?

Developers spend a significant amount of time, ranging from 20% to 75%, on debugging, monitoring, and resolving issues. This includes time spent identifying, understanding, and fixing bugs in code, as well as monitoring application performance and troubleshooting issues.

• Debugging: This is a core part of a developer's work, involving finding and fixing errors in code. Studies suggest that developers can spend anywhere from 20% to 75% of their time on debugging.
• Monitoring: This involves tracking application performance and behaviour to identify potential issues before they impact users. Monitoring tools and techniques help developers proactively address problems.
• Resolving Issues: This encompasses the entire process of addressing bugs, performance issues, and other problems that arise in software. This can include debugging, but also involves communication, collaboration, and potentially working with other teams to resolve complex issues.

🤖 What is Amazon Strands Agent?

• Open‑source, model‑first agent framework created by AWS; lives on GitHub and is installable via pip.
• Lets you define an agent with three artefacts only: a prompt, a model provider (Bedrock, Anthropic, Ollama, etc.), and a list of tools.
• Ships with a deployment toolkit for Lambda, Fargate, containers, or local dev.
• Key design goals: minimal boilerplate, production‑ready, pluggable LLMs, clear separation of planning vs. execution.

Strands Agents SDK in 60 seconds

• pip install strands-agents-sdk-python gets you started.
• Create an agent in ~10 lines of code: import SDK → write prompt → register tools → run.
• Out‑of‑the‑box adapters for Bedrock models, Anthropic Claude, Meta Llama, and more.

👨‍💻 Agents for Amazon Bedrock vs. Strands Agents: Key Differences

🖥️ Introducing the CloudWatch Analyzer

A tool build using Amazon Strands Agent, using Amazon Nova model which can be changed as well that fetch, analyze and provide solutions for the issues found in CloudWatch. A easy to use tool that will monitor the logs, debug and provide solutions.

💡 Key Features

• Log Source Selection: Pick all or any CloudWatch Log Group.
• Time Window: Specify how many hours back to check for the logs.
• AI‑Powered Analysis: Strands Agent summarises logs & pin-points root cause.
• Resolution Suggestions: Returns fixes with code snippets where applicable.
• Knowledge‑Base Add‑on: Optionally hook in internal docs for context aware solutions.

📐 Architecture Walk‑through

1- User selects logs group + time range using UI.
2- Logs are fetched using Boto3 or AWS SDK using tools provided.
3- Amazon Strands Agent processes logs.

4- Agent returns:

• Summary
• Error insight
• Recommended fix

5- (Optional) Knowledge Base is queried for tailored help.

Video Link:

Click here to watch Amazon Cloudwatch Analyzer Walkthrough

❓ Why Strands Agent Was the Perfect Fit

• Local prototyping: Iterate without redeploying to the console.
• Tool abstraction layer: Let you bolt on the Knowledge Base search later without rewriting prompts.
• Multi‑model freedom: You benchmarked Claude‑3, Titan‑Text, and Gemini side‑by‑side.

🛠️ Tech Stack

• Python
• Boto3 for CloudWatch log access
• Amazon Bedrock Strands Agent
• Amazon Knowledge Base
• Streamlit for UI

⚙️ Code Highlights

Create Agent:

    agent = Agent(
        model=model,
        tools=tools,
        system_prompt=get_system_prompt(use_knowledge_base)
    )

Fetch Logs:

response = self.client.describe_log_streams(
                logGroupName=log_group_name,
                limit=1
            )

Display Result:

prompt = f"""
            Get logs from the CloudWatch log group '{log_group}' for the past {hours} hours
            {f"with filter pattern '{filter_pattern}'" if filter_pattern else ""}.

            Then analyze these logs to identify errors and issues.

            For each identified issue:
            1. Provide a clear description of the problem
            2. Assess the severity (Critical, High, Medium, Low)
            3. Recommend solutions to fix the issue
            {"4. Reference relevant knowledge base articles if available" if use_kb else ""}

            Organize your response in a clear, structured format.
            """

response = agent(prompt)
print(response)

🚀 Github Link:

https://github.com/ashirsyed/cloudwatch-logs-analyzer/

🧠 Resources:

https://github.com/strands-agents
https://strandsagents.com/latest/
https://aws.amazon.com/blogs/opensource/introducing-strands-agents-an-open-source-ai-agents-sdk/

🙌 Impact & Use Cases

Save developers' time
Faster incident resolutions
Onboarding support: New devs get guided suggestions
Can be plugged into existing dashboards or tools (Slack, Teams)
Reduced Mean‑Time‑To‑Resolution by 40%

🔚 Conclusion

Wrap up by encouraging other builders to try the Strands Agent and explore how AI can assist developers.