Deep dive into NinjaTech AI's enterprise agent stack:
Core architecture:
- Long-running agents (persistent state, not ephemeral)
- Native Slack integration for team workflows
- Full on-prem deployment (zero data leaves your infra)
- Significantly lower TCO vs cloud-hosted LLM APIs
Why this matters: Most AI agents today are stateless request-response loops. Long-running = they maintain context across sessions, handle async tasks, and don't reset every time. On-prem means compliance teams actually approve it.
Practical use case: Deploy in your Slack, agent monitors channels, executes multi-step workflows (code reviews, ticket routing, data pulls), all without sending logs to OpenAI/Anthropic.
Cost angle: Running local inference (likely Llama/Mistral fine-tunes) cuts per-token costs by ~10-100x vs API calls at scale.
Hour-long walkthrough + live demo available. Worth watching if you're building internal tooling or evaluating agent frameworks for production.
Core architecture:
- Long-running agents (persistent state, not ephemeral)
- Native Slack integration for team workflows
- Full on-prem deployment (zero data leaves your infra)
- Significantly lower TCO vs cloud-hosted LLM APIs
Why this matters: Most AI agents today are stateless request-response loops. Long-running = they maintain context across sessions, handle async tasks, and don't reset every time. On-prem means compliance teams actually approve it.
Practical use case: Deploy in your Slack, agent monitors channels, executes multi-step workflows (code reviews, ticket routing, data pulls), all without sending logs to OpenAI/Anthropic.
Cost angle: Running local inference (likely Llama/Mistral fine-tunes) cuts per-token costs by ~10-100x vs API calls at scale.
Hour-long walkthrough + live demo available. Worth watching if you're building internal tooling or evaluating agent frameworks for production.