Inspiration
Modern enterprises operate thousands of workflows, yet most automation systems remain static and brittle. We wanted to explore what true agentic infrastructure could look like using Amazon Nova—an AI system that doesn’t just run tasks, but designs, monitors, and improves its own operational workflows.
What it does
Nova Nexa is a self-evolving enterprise operations agent. Users describe a goal in natural language—such as “analyze new Amazon Nova announcements and produce a weekly report”—and the system automatically generates a multi-agent workflow graph that executes the task.
The platform dynamically decomposes the goal into specialized agents, executes the workflow, streams events in real time, and repairs or regenerates failing nodes automatically. This allows enterprises to move from static automation to adaptive AI-driven operations.
How we built it
Nova Nexa is built as an agent orchestration framework powered by Amazon Nova models through Amazon Bedrock.
- Nova Pro acts as the reasoning “queen” agent that plans tasks and generates workflow graphs.
- Nova Micro agents execute specialized operations such as retrieval, analysis, and reporting.
- A dynamic task graph engine converts user goals into executable pipelines.
- Event streams and observability layers track agent execution in real time.
- A self-healing loop detects failing nodes and regenerates them using LLM reasoning.
The result is a system where workflows are not manually scripted—they are generated and evolved by AI agents.
Challenges we ran into
Designing reliable agent orchestration was one of the hardest challenges. LLM agents can be powerful but unpredictable, so we had to build safeguards including:
- structured task graphs for deterministic execution
- real-time event monitoring and telemetry
- automatic node regeneration when failures occur
Balancing autonomy and reliability was the key engineering challenge.
Accomplishments that we're proud of
- A working self-evolving agent workflow system
- Real-time event streams for observing agent decisions
- Automatic regeneration of failed workflow nodes
- A flexible architecture capable of scaling to large enterprise operations
What we learned
Building agent systems revealed that reasoning alone is not enough. Successful agentic systems require strong infrastructure: observability, structured planning, and feedback loops that allow agents to improve their own workflows.
What's next for Nova Nexa: Self-Evolving Enterprise Ops Agent
Next we plan to expand Nova Nexa into a full enterprise AI operations layer, including:
- integrations with enterprise tools and data platforms
- long-running autonomous workflows
- multi-agent collaboration across departments
- adaptive learning from workflow outcomes
Our vision is a future where enterprises can describe goals and AI systems design, run, and continuously optimize the workflows required to achieve them.
Built With
- css
- powershell
- python
- shell
- tsql
- typescript
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