Generative AI has captured the world’s imagination — but we’re now moving past the phase of cool demos and into the reality of enterprise deployment.
At esynergy, we’ve had the privilege of working across industries like Insurance, Asset Management, and the Public Sector, and one thing is clear: building real GenAI systems is complex, multidisciplinary, and incredibly rewarding.
To succeed, you need more than just a powerful model. You need a clear lifecycle framework — one that includes not only the model itself, but also the humans, tools, agents, data pipelines, and governance structures around it.
Here’s how we approach that lifecycle — and how it came to life in a recent Insurance underwriting project.
The GenAI & multi-agent system lifecycle (8 steps that matter)
This is the high-level map we use when helping teams build GenAI systems with real-world impact:
| Stage | Purpose | Tools & languages | What success looks like |
| 1. Use case discovery | Identify valuable, feasible GenAI opportunities | Azure Data Factory, Miro, Business Canvas | Clear mapping of business needs to AI capabilities |
| 2. Data ingestion & preparation | Clean, transform, and embed structured/unstructured data | AWS S3, Pandas, Pinecone, Airflow | Vector-ready datasets with metadata |
| 3. Model selection & fine-tuning | Customize LLMs for domain tasks | GPT-4, LoRA, HuggingFace, YAML | Improved F1/accuracy on domain-specific prompts |
| 4. Model Context Protocol (MCP) | Dynamically assemble memory, tools, and goals | LangChain, Semantic Kernel, DSPy | Agents access the right context in real-time |
| 5. Multi-agent orchestration | Coordinate agents to solve subtasks | CrewAI, AutoGen, LangChain Agents | Reliable agent collaboration and tool use |
| 6. Feedback loop & learning | Improve models/prompts using logs and corrections | LangSmith, RLHF, PromptLayer | Reduced hallucinations and faster performance |
| 7. Deployment & monitoring | Serve APIs reliably and observably | FastAPI, Kubernetes, Grafana | Sub-second response times, explainable outputs |
| 8. Governance & compliance | Ensure responsible and auditable AI | Azure Purview, AWS Macie | Transparent, documented, and regulated AI use |
Let’s walk through these — and I’ll bring each one to life with a real example from an insurance client.
Case study: Automating underwriting in commercial insurance
Imagine you’re an underwriter. Your inbox is full of broker emails, each with 100+ page PDF attachments (MRCs, SoVs, claims history). You need to extract risk details, assess them, check pricing guidelines, and flag any red flags — fast.
Our client wanted to automate as much of that as possible using GenAI and agents.
- Use case discovery
This is the phase where it’s tempting to build a chatbot. Don’t.
We spent time with real underwriters, watched their workflows, mapped out pain points, and discovered the opportunity wasn’t just “chat” — it was intelligent document understanding and decision support.
Success: Everyone (business + tech) agreed on the opportunity and scope.
- Data ingestion & preparation
We pulled data from SharePoint, Outlook, and internal systems. PDFs were messy. Some were scanned. We used LangChain document loaders, embedded them using OpenAI’s API, and stored everything in Pinecone.
Languages & Tools: Python, Pandas, Azure Data Factory, Pinecone
Success: A searchable, vectorized knowledge base ready for prompting
- Model selection & fine-tuning
We experimented with base GPT-4 and realized it struggled with underwriting jargon. So we fine-tuned a smaller open model on anonymized MRCs, labeled risks, and sample clauses.
Result: Entity extraction accuracy went from 60% to 87%
- Model Context Protocol (MCP)
This is where it gets interesting.
We implemented an MCP layer — basically a system that injects context (previous quotes, compliance rules, limits) dynamically before the model sees the prompt. It made a huge difference in performance and trust.
Tools: LangChain, Semantic Kernel, vector DB for context memory
Success: Responses were more relevant, complete, and auditable
- Multi-agent orchestration
We didn’t build one big agent. Instead, we created a team:
- Document Reader Agent for parsing PDFs
- Risk Assessment Agent for applying rules
- Compliance Agent for clause validation
They talked to each other using a shared task queue.
Success: Each agent did one thing well — and worked together
- Feedback loop & learning
This wasn’t “set it and forget it.” Underwriters could flag incorrect answers. Those were logged and used to update prompts and embeddings weekly.
Tools: LangSmith, PromptLayer
Success: The system got smarter over time without constant fine-tuning
- Deployment & monitoring
We deployed everything using Azure Functions + FastAPI. Agents could be triggered from the underwriting dashboard. Grafana showed latency, failure rates, and usage trends.
Success: Response time < 2s, 99.9% uptime, real-time logging
- Governance & compliance
This mattered most.
All model outputs were logged. We tracked what data the agents used, anonymized sensitive info before model inference, and added human-in-the-loop approvals for borderline cases.
Success: We passed internal audit — and got underwriter buy-in
So, what did we learn?
GenAI works — but only when you treat it like a real system.
It’s not just about large language models. It’s about lifecycle thinking, orchestration, compliance, and most importantly — aligning AI with human workflows.
If you’re exploring GenAI in your business — especially in a regulated space — I’d love to hear what’s working (or not) for you.
Let’s build systems that don’t just impress but endure.