Agentic AI Proof-of-Concept

Accelerate your journey from prompt to production with AWS-native agentic systems.

Agentic AI changes the game. Beyond simply predicting the next word, Agentic AI coordinates actions, integrates tools, and reasons across steps. New Math Data’s Agentic AI Proof-of-Concept (PoC) gives your team the ability to build and test a fully functional agent system in just a few weeks from funding application approval. This PoC focuses on orchestrated workflows, human-in-the-loop design, and real-world utility, all while running securely in your AWS environment.

This PoC engagement is structured, scoped, and optimized for a single use case with clearly defined business outcomes and a roadmap for scaling to production.

Engagement Steps

1. Use Case Framing & Agent Design. We start by translating your business need into an agentic task system. This includes decomposing the target workflow into subtasks, identifying required tools (APIs, RAG pipelines, internal services), defining agent roles, and aligning output expectations with measurable KPIs. Outputs include:

• Agent goal hierarchy and task map
• Success criteria and failure modes
• HITL or governance checkpoints
• Tool and data source inventory

2. AWS-Native Environment Provisioning. We deploy a secure PoC environment using a turnkey Terraform module, tailored to your cloud architecture.

3. Model Integration & Tool Wiring. We configure one or more foundation models (Claude, GPT, Llama via Bedrock) and wire them into the system using Amazon Bedrock AgentCore. This includes:

• Connecting agent memory and state via Strands Agents’ memory module or external vector store (via RDS/ElasticSearch, etc.)
• Defining agent tool schemas and API wrappers
• Prompt engineering and structured tool call configuration
• Testing tool invocation via simulated agent runs

4. Agent Buildout & Workflow Implementation. We implement the actual agent behavior using structured planning and orchestration logic.

• Authoring agent plans and fallback logic in AgentCore
• Integrating custom or third-party tools
• Incorporating human-in-the-loop checks for specific decision points

5. Validation, Iteration & Handoff. We run the agent against realistic inputs to validate performance, tune the orchestration logic, and prepare for broader deployment. We deliver:

• A functional agent POC running in your AWS account
• Evaluation summary, prompt library, and source code
• Recommendations for scaling, monitoring, and role-based access control
• Full documentation and production roadmap recommendations

This POC is Ideal for

• Organizations with a high-value use case and real data
• Teams looking to go beyond chatbots and adopt structured reasoning workflows
• Business units seeking qualification of potential ROI before a larger GenAI investment
• Innovation leaders exploring autonomous systems with oversight

Common Agentic AI Use Cases

• Customer Support Agents: Agents that triage, classify, and respond to tickets with retrieval + HITL oversight
• Content Ops Agents: End-to-end systems for summarizing, rewriting, and routing communications
• RAG Assistants: Domain-specific research assistants using internal sources for policy, legal, or healthcare domains
• Knowledge Navigation Agents: Semantic interfaces that answer questions across data silos
• Action-Oriented Agents: Workflow agents that trigger follow-up actions, assign owners, or escalate issues
• Document Insight Agents: PDF analysis agents that extract, compare, and summarize unstructured filings

Outcomes You Can Expect

• Working Agent System: A functional, AWS-native Agentic AI system aligned to your business objectives.
• Tangible Business Value: Demonstrable results to build internal momentum and support further investment.
• Scalable Architecture: A modular, cloud-native foundation that supports future agents, tools, models, and use cases.

Why New Math Data

• AWS-Native, Agentic by Design: We don’t just experiment with models, we engineer entire systems using Bedrock AgentCore, Strands, and other native AWS services that meet enterprise security and scale requirements.
• Real-World Experience: From energy and financial services to education and life sciences, our agent deployments are grounded in operational complexity and regulatory realities, not just sandbox experimentation.
• Design for Adoption: We help you plan for the full lifecycle: architecture, monitoring, human review, failure handling, and governance. Our agents aren’t just demos, they’re designed to be trusted and deployed.