HCLTech CostProfiler Advisor for Agentic AI

Managing the cost of agentic AI applications is complex. Unlike traditional software, AI agents incur variable expenses across multiple dimensions — foundation model tokens, tool invocations, knowledge base queries, and infrastructure overhead. Engineering teams need visibility into these costs to make informed architectural decisions, while finance teams require accurate forecasts to budget effectively.

CostProfiler Advisor is a conversational AI agent that transforms cost data into actionable insights. Built on Claude using Amazon Bedrock and deployed to Amazon Bedrock AgentCore Runtime via the Strands framework, it answers natural-language questions about your agentic AI project costs with precision and context.

The agent retrieves real cost breakdowns from DynamoDB, categorizing expenses by LLM tokens, tool calls, knowledge base queries, and infrastructure. It queries Monte Carlo forecast data to cite P50 expected costs, P90 budget thresholds, and P95 ceiling projections for monthly spending. When teams evaluate alternatives, the agent compares pricing across Bedrock foundation models including Claude, Titan, and Llama for any token volume scenario.

Beyond cost data, CostProfiler Advisor surfaces observed runtime metrics such as average input and output tokens per session, tool call frequency, knowledge base query patterns, and agent step counts. Every response is grounded in your project's measured data, ensuring recommendations reflect actual usage patterns rather than theoretical estimates.

The solution integrates seamlessly through a dashboard API endpoint that connects to AgentCore Runtime. Teams interact with the advisor conversationally, asking follow-up questions and exploring cost drivers without writing queries or building reports. This approach dramatically reduces the time required to understand cost trends and identify optimization opportunities.

CostProfiler Advisor leverages Amazon Bedrock for foundation model access, DynamoDB for cost and metrics storage, and Amazon S3 for forecast data persistence. The architecture maintains data isolation and includes audit trails for compliance. All data remains within your AWS environment, with encryption at rest and in transit.

HCLTech brings deep expertise in AI agent development and AWS cloud architecture. Our team has deployed production-grade agentic systems across industries, combining technical implementation with operational best practices. This solution reflects our understanding of the unique challenges in managing AI economics at scale.