Agentic AI for The Unmanned Enterprise

Agentic AI for The Unmanned Enterprise

• 1. Agentic AI Consulting | Development | Migration for The Unmanned Enterprise

• 2. Proven AWS Partner Gen AI Competency Launch Partner Migration SCA ML Competency Partner Within 2 years of operations

• 3. Stage 1 The Adaptive Enterprise. Stage 2 The Proactive Enterprise. Stage 3 The Unmanned Enterprise. 3 Stages of Agentic AI Adoption

• 4. The Adaptive Enterprise The Proactive Enterprise The Unmanned Enterprise 3 Stages of Agentic AI Adoption Simple use cases Internal users Semi-automated agent workflows Human-in-the-loop Complex workflows Internal and external users Fully automated multi-agent workflows; minimal HITL Proven and consistent AI agent architectures Autonomous goal-driven multiagent teams acting on triggers Collaborative, through the supply chain Agent first operating models and processes * Looks like a lead from eCommerce. Let me forward this to Sam with my research about the company. That will give Sam context about why the lead is interested now. * We’ve got 19 leads from the eCommerce Ads campaign. I will ping Sam and tell him to increase budget to the campaign. * Only one acquisition campaign is driving results. Let me check CRM... zero deals from that campaign. But 32% of our new pipeline is from the outbound channel in same timeframe. I’ll stop Ads and add capacity to outbound channels.

• 5. 3 Stages of Agentic AI Adoption 95% will deliver zero RoI 40% of all AI agent projects will die here Stage 1 The Adaptive Enterprise. Stage 2 The Proactive Enterprise. Stage 3 The Unmanned Enterprise. Source: Gartner

• 6. GoAutonomy Introducing GoML’s Multi-Agent Agentic AI Accelerator for The Unmanned Enterprise

• 7. Why customers need it? Get RoI even when you operate at scale Scale into Stage 2 Avoid death-by-1000 POCs Escape Stage 1 stagnation Building trust among users with accuracy and functionality Adoption GoAutonomy

• 8. LLM on Bedrock Agent 1 Agent 2 Agent 3 Agent 4 Multi-Agent Supervisor User AWS ECOSYSTEM P API Gateway roven multi-agent architecture Templates for use cases and verticals AWS and Bedrock integrations Wide database support Best practices Proven implementations Proven RoI from production Powered by our proven AI Matic delivery framework for 95% Day 1 accuracy Value proposition GoAutonomy

• 9. Picking low value use cases Dependent on too few users Zero attention to agent architecture Building first, pushing adoption next Avoid POC death with GoML and GoAutonomy Getting to Stage 2 with Agentic AI

• 10. GoAutonomy: Case study on adaptation for Ojje Ojje wanted to increase the consumption and retention of educational and recreational content by children. Ojje achieved that through self-guided interactive books. Their existing generative AI solution left a lot to be desired because: 1.It took over two days to go from ideas to produced books. 2.Personalization in the stories was minimal. 3.The quality of illustrations was left wanting. Ojje was unable to generate hyper-personalized, high-quality books for each child, parent, and teacher’s needs at scale. Ojje wanted to address this trifecta of hard challenges with a reimagined agentic AI approach.

• 11. Ojje: Agentic Workflow

• 12. CONTENT GEN (BEDROCK) AGENTS IMAGE GEN AGENTS AUDIOBOOK CREATOR VOCABULARY LIST CREATOR PAGE WISE STORY CREATOR COMPREHENSION Q & A PAGE BACKGROUNDS SCIENCE OF READING DECODABLES CHARACTERS BOOK COVERS TITLE AND SUMMARIES SUPERVISOR AGENT CONTENT LOCALIZATION AGENTS Ojje: Agents

• 13. Build your Agentic AI, the right way Migrate to and scale on AWS and with right-fit LLMs Scale: Agentic AI POCs and pilots in 2 weeks with proven architecture Speed: Enterprise-secure for highly regulated industries Security: GoAutonomy

• 14. Asynchronous, Event-Driven Architecture: Prevents LLM latency from blocking the UI, providing a responsive user experience. The user gets an immediate acknowledgment while the agent "thinks." Multi-faceted Search: Relying solely on vector search is insufficient for many use cases. A hybrid approach delivered the best of both worlds: semantic understanding and precise filtering. Stateful Conversations: Persisting conversation state in MongoDB is essential for maintaining context and enabling true multi-turn dialogue for conversational use cases. Orchestration: The Supervisor doesn't just assign tasks; it should manage the flow of information. The output of one agent becomes critical input for multiple downstream agents. MongoDB is a good choice for single source of truth. What worked Lessons from GoAutonomy customer builds

• 15. S inglePrompts:EarlyAI agentbuildsused one large,complexprompttodoeverything. This was slow, expensive, and unreliable. We shifted to a multi-agent approach where smaller, specialized LLM calls handle distinct tasks (NLU, clarification, response generation). Stateless Design: Our early prototypes were stateless, treating every user message as a new query. This failed to capture user context and led to frustrating, repetitive interactions. Adopting a stateful design was a turning point for user experience. Throttling: Model throttling will cre ate production bottlenecks. You need to build a smart queuing mechanism to ensure reliability under all conditions. This is particularly important for image and video generation agents. What failed Lessons from GoAutonomy customer builds

• 16. Get a fully funded Agentic AI POC Assessment and Discovery → Strategic workshops to clarify use cases, align stakeholders & build an agentic AI roadmap Proof of Concept (POC) → 4-week sprints to validate ideas, showcase value and secure buy-in Pilot Development → Extend POCs into scalable AI agent pilots, bridging experimentation and production Get started on the path to The Unmanned Enterprise with exclusive funding opportunities. Limited seats only.

• 17. Contact Us: marketing@goml.io | Visit Us: www.goml.io Agentic AI Consulting | Development | Migration for The Unmanned Enterprise