GoML's Agentic AI Accelerator enables enterprises to build autonomous AI systems in just 2 weeks, deploying complex workflows 10X faster than traditional approaches. While 70% of AI agent projects fail to reach production and 40% won't scale by 2027, this solution addresses critical gaps in architecture, orchestration, and enterprise scalability. The accelerator features a supervisor agent for central coordination, specialist agents for specific tasks, and robust state management with constraint enforcement. Built on AWS with Amazon Bedrock integration, it supports Claude, GPT, Nova, and emerging models while maintaining data sovereignty and enterprise security compliance. Proven use cases include software testing automation (Git, CI/CD, Jira integration), interactive content publishing (reducing creation time from 2 days to 1 hour), and financial data analytics with natural language querying. The framework offers full audit trails, governance workflows, and multi-model flexibility without vendor lock-in. With 20+ successful deployments across healthcare, finance, and education sectors, the accelerator provides battle-tested agent patterns and proven architectures. The structured 4-month roadmap includes discovery workshops, pilot development, and production deployment with enterprise governance, enabling organizations to move beyond simple chatbots to complex autonomous systems. Agentic AI accelerator: https://www.goml.io/agentic-ai-accelerator

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 multi-
agent 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
API Gateway
Proven 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. Single Prompts: Early AI agent builds used
one large, complex prompt to do everything.
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