Nemo AI - The First Draft
In today’s fast-paced software landscape, engineering teams spend nearly 65% of their time on routine, repetitive, and low-value tasks — debugging small issues, writing boilerplate code, or creating reports. That’s time better spent innovating.
Nemo AI changes that equation. Nemo AI is an autonomous multi-agent system that automatically transforms Jira stories into ready-to-review GitHub pull requests, empowering teams to cut feature delivery time in half while maintaining quality and control.
Why Nemo AI?
Every developer has faced the friction between planning and production — translating a business requirement into real, reviewable code. Nemo AI bridges that gap.
When a Jira story moves to “In Progress”, Nemo reads it, understands the business context, analyzes your codebase, and creates a pull request with the first draft of the implementation — complete with documentation, architecture diagrams, and tests.
The result? Developers spend more time reviewing, refining, and innovating, and less time handling the grunt work.
🚀 The First Draft
Nemo AI acts as a virtual software engineer that:
- Interprets Jira requirements.
- Analyzes your GitHub repository, internal docs, and Confluence pages.
- Plans, writes, and peer-reviews code using its specialized AI agents.
- Opens a GitHub PR ready for developer review.
This process not only reduces cycle time but ensures consistency in coding standards, documentation, and architectural decisions.
Built for the Modern Enterprise Stack
Nemo integrates seamlessly with tools already central to your workflow: Jira, GitHub, Confluence, AWS, and MCP servers.
It’s not another platform to manage — it’s the connective tissue that automates development tasks using the tools your team already trusts.
Inside Nemo’s Architecture
While Nemo is built for simplicity on the surface, under the hood it runs on a serverless, event-driven architecture that scales effortlessly.
Core Components
- AWS Lambda & ECS Fargate – Executes tasks based on complexity and duration.
- Amazon SQS – Queues tasks for asynchronous, decoupled processing.
- DynamoDB – Tracks Jira stories and workflow states.
- S3 – Stores artifacts, reports, and logs.
- AgentCore Code Interpreter – Secure Python sandbox for code execution.
- Bedrock AgentCore Observability – Full visibility via OpenTelemetry traces.
When a Jira webhook fires, a Lambda ingests the data, routes it (short vs. long task), and delegates the job to Nemo’s to create the PR.
Multi-Agent Intelligence
Nemo AI mimics the way real teams work — with specialized agents collaborating asynchronously:
- Planner Agent – Interprets Jira stories and crafts implementation plans.
- Senior Engineer Agent – Writes the actual code using MCP-backed context.
- Reviewer Agents – Evaluate security, design, standards, and algorithms.
- Scoring Agent – Validates the solution against business requirements.
- Documentation Agent – Generates clear PR descriptions and technical notes.
Integration with MCP Servers
To prevent hallucinations and outdated code, Nemo connects to:
- Context7 MCP for real-time library documentation.
- AWS Knowledge MCP for up-to-date AWS best practices and service APIs.
This ensures Nemo always writes accurate, production-ready code aligned with the latest standards.
Observability & Trust
Every agent interaction emits OpenTelemetry traces to Amazon Bedrock AgentCore Observability, enabling full transparency into how Nemo made each decision — from reading a Jira story to creating a pull request.
Developers can inspect every step, making Nemo not just autonomous, but auditable and trustworthy.
Why This Matters for Businesses
Nemo AI is not just a developer productivity tool — it’s a strategic advantage.
- Faster Feature Delivery: 50% reduction in development cycles.
- Consistent Quality: AI-enforced standards and best practices.
- Data Democratization: Business teams get insights without data science bottlenecks.
- Scalable Operations: Serverless architecture keeps costs low and performance high.
By automating low-value tasks, Nemo frees developers to focus on architecture, innovation, and impact — the kind of work that drives competitive differentiation.