𝗔𝗺𝗮𝘇𝗼𝗻 𝗕𝗲𝗱𝗿𝗼𝗰𝗸 𝗔𝗴𝗲𝗻𝘁𝗖𝗼𝗿𝗲 𝗪𝗲𝗯 𝗦𝗲𝗮𝗿𝗰𝗵: 𝟳 𝗠𝗶𝘀𝘁𝗮𝗸𝗲𝘀 𝗧𝗵𝗮𝘁 𝗞𝗶𝗹𝗹 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗔𝗴𝗲𝗻𝘁𝘀
Most AI teams made a mistake in 2024. They built RAG pipelines that rely on static data.
A static RAG pipeline is like a photograph of the internet. It is outdated the moment you take it. AWS just changed this with Web Search on Amazon Bedrock AgentCore.
This tool lets your agents use live data without you building search infrastructure. However, many teams fail during deployment.
Here are the 7 mistakes you must avoid:
Using web search as a replacement for RAG. Web search is for current events and pricing. RAG is for your internal company documents. Use a router to choose the right path for each query.
Assuming Bedrock Guardrails cover web search. They do not. Web search is a separate path. You must set up AgentCore policy controls like domain allowlists and PII scrubbing yourself.
Running redundant searches in multi-agent systems. In frameworks like AutoGen, every sub-agent might call search separately. This inflates your costs by 4x to 8x. Use a shared search memory instead.
Ignoring the Frozen Knowledge Trap. Do not blame your model when it gives old answers. The problem is likely your data architecture. If the answer changes weekly, you need live search.
Skipping observability. If your agent hallucinates, you need to know why. Was it a bad search result or a model error? Use Langfuse to trace every step.
Hardcoding against specific endpoints. AWS will update these tools. Use MCP-compatible tool descriptors so you can swap providers easily.
Failing to test for prompt injection. A poisoned webpage can hijack your agent. Test your agent with known injection payloads before you go live.
How to build a production-ready agent:
- Classify the query intent.
- Route to RAG, Web Search, or Memory.
- Pass web results through a policy filter.
- Assemble the context and call the model.
Stop building static systems. Move toward live, grounded agents.
Optional learning community: https://t.me/GyaanSetuAi