What This Tool Does
Real examples of how the connector helps your AI agent take action; like sending messages, updating records, or syncing data across tools.
Real-Time Lookup
Instantly access foundational model status, endpoint activity, or invocation logs from AWS Bedrock.
Example
"Check the availability and response time for the Claude model endpoint."
Memory Recall
Retrieve past foundation model outputs and tuning sessions.
Example
“Get Bedrock model completions from the last fine-tuning round.”
Instant Reaction
Alert ML team when prompt completions in AWS Bedrock spike in latency.
Example
"Notify team if LLM response time >2 seconds on critical endpoint."
Autonomous Routine
Monitor prompt usage and model cost breakdowns.
Example
"Generate weekly prompt usage and token spend report."
Agent-Initiated Action
Auto-throttle or reroute prompts to cheaper models.
Example
"Switch to lower-cost model if usage exceeds limit."
Connect with Apps
See which platforms this connector is commonly used with to power cross-tool automation.
Amazon Sagemaker
Serve models via Bedrock for inference
OpenAI
Use foundation models for embeddings
Slack
Notify ML team on inference failures
Try It with Your Agent
Example Prompt:
"Send prompts to AWS Bedrock and alert Slack if inference latency exceeds 5 seconds."
How to Set It Up
Quick guide to connect, authorize, and start using the tool in your Fastn UCL workspace.
1
Connect AWS Bedrock in Fastn UCL: Navigate to the Connectors section and select AWS Bedrock, then click Connect.
2
Authenticate with AWS credentials to access foundation models.
3
Enable “generate_text” and “embed_content” in the Actions tab.
4
Use the AI Agent to generate responses or vectorize data by issuing relevant prompts.
Why Use This Tool
Understand what this connector unlocks: speed, automation, data access, or real-time actions.
