𝗔𝗺𝗮𝘇𝗼𝗻 𝗕𝗲𝗱𝗿𝗼𝗰𝗸 𝗔𝗴𝗲𝗻𝘁𝗖𝗼𝗿𝗲 𝗪𝗲𝗯 𝗦𝗲𝗮𝗿𝗰𝗵: 𝟳 𝗠𝗶𝘀𝘁𝗮𝗸𝗲𝘀 𝗧𝗵𝗮𝘁 𝗕𝗿𝗲𝗮𝗸 𝗔𝗴𝗲𝗻𝘁𝘀
Your AI agent is not hallucinating. It is reciting old facts.
Most teams build agents that work in demos but fail in production. They rely on stale training data or third-party search APIs that leak private data.
Amazon Bedrock AgentCore Web Search solves this by keeping web retrieval inside your AWS boundary. It is infrastructure, not just a tool.
Avoid these 7 deployment mistakes to keep your agents accurate and safe:
- Replacing Vector Databases Web search cannot find your private data. It only finds public info. Use a vector database for internal knowledge and AgentCore for real-time news.
- Static Source Selection Do not turn web search on for every query. This wastes money and adds latency. Use a lightweight classifier like Claude Haiku to route queries. Only call the web when you need fresh data.
- Loose IAM Permissions Managed does not mean secure. Do not use wildcard permissions. Scope your IAM roles to specific agent ARNs to prevent runaway costs.
- Ignoring Source Authority Web search can find a random blog instead of an official document. Use Bedrock Guardrails to create a domain allowlist. Force your agent to provide citations.
- Sequential Retrieval Web search adds 800ms to 1.4s of latency. If you wait for the search to finish before reasoning, your agent will feel slow. Use async patterns to fetch data in parallel.
- Failing to Monitor Drift Accuracy drops as the world changes. Track your retrieval sources and citation domains in CloudWatch. Test your models monthly to catch quality drops before customers do.
- Unbounded Search Loops Multi-agent loops can trigger endless web calls. One startup spent $11,000 in one month because of this. Set a hard limit on web calls per session using a Lambda budget.
The goal is a hybrid stack: • Intent Classifier • Internal Vector DB • AgentCore Web Search • Bedrock Guardrails • Drift Monitoring
Build for month six, not just for the demo.
Optional learning community: https://t.me/GyaanSetuAi