AI agents are changing what software can do. A chatbot answers questions. An agent reasons through a task, calls business systems, and takes action on a user's behalf. For a startup, this creates a real opportunity to deliver more with a small team. It also creates real risk, because an agent that acts on business data needs the right controls around it. Building an agent prototype now takes days. Running one reliably, securely, and at a predictable cost is where most small teams get stuck.
On September 20, 2026, NileForge hosted the first edition of NileForge AI Connect in Jaipur. Startup founders, AI practitioners, and technology teams came together to discuss how to build AI-first companies and how to take AI agents from prototype to production on AWS.
In this post, we share the key takeaways from the event.
About NileForge AI Connect
NileForge AI Connect is an invitation-only forum for founders and teams building with AI. We keep each edition small so that attendees can engage directly with speakers and with each other. Edition 01 brought together AI and machine learning practitioners, founders of growing startups, and early-stage teams working on their first prototype.
Building an AI-first startup
The event opened with a keynote discussion with startup founders on building a company from the ground up. The speakers shared the following guidance:
- Validate before you build. Confirm that the problem is real and that customers will pay to solve it before investing in a solution.
- Earn product-market fit with evidence. Track adoption and retention rather than relying on early interest.
- Apply AI where it changes an outcome. AI creates value when it improves a customer or business result, not when it is added as a feature.
- Use government startup programs. These programs can extend runway and build early credibility, yet many founders leave them unused.
Taking AI agents from prototype to production
NileForge's CTO led a live session on taking an AI agent from a working prototype to a production-ready system on AWS. The demo featured an agent that lets customers and administrators search and manage a product catalog in natural language, a common use case for e-commerce and retail businesses. The agent ran on Amazon Bedrock AgentCore and used business functions as tools through the Model Context Protocol (MCP).
The session made one point clear: the agent is only a small part of a production system. Because an agent acts on business data, the system around it must control what it can do, measure how well it does it, and show every step it takes. The session covered the production controls that make this possible:
- Identity and fine-grained access control. Every request is authenticated, and the agent can use only the tools and data the signed-in user is authorized to access. A customer and an administrator get different capabilities from the same agent.
- Governed tool access. The agent reaches business systems through a managed gateway rather than direct connections, so every tool call is routed, checked, and logged.
- Evaluation with curated datasets. Teams measure whether the agent selects the right tools and completes tasks correctly, and re-run these evaluations whenever prompts or models change.
- Guardrails. Policies keep responses and actions within defined boundaries and reduce the risk of hallucinations.
- End-to-end observability. Traces and metrics show each step the agent takes, so teams can diagnose failures and track cost per task.
- A/B testing and controlled releases. New versions reach a portion of users first, so teams can improve the agent without putting customers at risk.
The CTO also shared lessons from client work, including reliability issues that appear only under real usage, and the design decisions that keep multi-step agent workflows predictable in both behavior and cost. A key message for startups was that managed AWS services now handle much of this foundation, so a small team can build production-grade controls without a dedicated platform team.
How NileForge helps startups build with AI
Manish Pandya, CEO and founder of NileForge, closed the event by sharing how NileForge works with startups and small businesses on AWS. Most early-stage teams do not have the time or specialist engineers to design cloud and AI architecture while also shipping a product. NileForge fills that gap. We help teams design, build, and run generative and agentic AI applications on AWS, along with the cloud foundations and data platforms those applications depend on.
Our team has taken AI into production for fintech and insurance clients, including a generative AI underwriting assistant for a commercial insurer. We bring the same practices to every startup we work with: security, evaluation, and cost control are built into the first design rather than added after launch. This helps founders reach production sooner, avoid expensive rework, and scale on AWS with confidence as their business grows.
Conclusion
Edition 01 of NileForge AI Connect gave founders and technology teams in Jaipur a practical view of what it takes to run AI agents in production. It is the first in an ongoing series. To hear about future editions, follow NileForge on LinkedIn.
If you are building an AI agent or another generative AI application, NileForge offers a complimentary Prototype-to-Production Review for startups. The review combines the AWS Well-Architected Framework with a generative AI readiness assessment to identify what your application needs to run reliably in production. Learn more about our Generative & Agentic AI services, or contact our team to request a review.