Introduction
Frameworks like llama-stack and langchain allow for quick prototyping of generative AI applications. However, transitioning generative AI prototypes to production requires solving different challenges: Companies demand robust security, testability, traceability, and evaluation capabilities of their AI logic.
What Problem does PhariaEngine solve?
Problem
Most PoCs that develop AI methodology wrap a python-based framework into isolated containers with custom web servers. While this works, it creates redundancies: reinventing foundational components (authentication, tracing, scaling), creates unique dependencies per container requiring constant updates & synchronization, scaling inefficiently due to redundant & divided resource allocation
Solution
This is the problem PhariaEngine solves. By providing a unified runtime environment, PhariaEngine eliminates redundancy & provides a constrained, but capable interface each Skill can interact with. PhariaEngine:
- Unifies foundational components (authentication, tracing, scaling) into a single runtime environment
- Simplifies deployment using OCI registries & modern CI/CD practices
- Allows developers to focus on the methodology development of the business logic
USPs - From Prototype to Production in One Secure Platform
As outlined above, it is fairly easy to put together PoC AI Skills. However, such PoCs are often a security-risk, lack scalability and will become a maintenance nightmare.
PhariaEngine redefines production-grade AI deployment by allowing for rapid prototyping & their immediate transformation into secure, scalable, and maintainable AI Skills. Making it the system of choice for deploying hundreds of AI Skills in a secure, scalable and maintainable way
| Seconds to Production | Seamless PhariaAI Integration | Built-in Security & Compliance |
|---|---|---|
| Auto-Scaling Resources | Agent-Ready Infrastructure | Minimal Dependency Maintenance |
1. Speed of Deployment
PhariaEngine allows developers to focus on the problems they are trying to solve. Developers iterate on methodology, not infrastructure.
Once developers are happy with their methodology, a new or updated Skill can be brought into production in seconds.
2. Scalability
Skills in PhariaEngine run as serverless components. This means your code can serve many requests in parallel. Blocking calls (e.g. due to length) to inference, which could become a problem in other AI applications, wont prevent your Skill from being accessible using PhariaEngine.
Serverless components also mean that you do not need to worry about uptime and scaling, as PhariaEngine will allocate resources dynamically between Skills.
3. Maintainability
PhariaEngine provides built-in implementations for common protocols (HTTP, gRPC) and file standards, minimizing third-party dependencies. Less dependencies mean less maintenance burden to keep dependencies up to date.
4. Security
AI applications become most useful when they have access to your knowledge base. This is also when they become a security risk, as this knowledge must not be exposed to unauthorized persons.
Skills operate in a sandboxed environment with restricted I/O permissions. Explicit data access policies and audit trails mitigate risks when integrating with sensitive knowledge bases or external APIs. This allows developers to be creative in their methodology while minimizing the attack surface.
5. Compliance
PhariaEngine exposes some unified configuration options that enforce consistency across all Skills. Metrics, telemetry, authentication and inference backends are handled globally, reducing per-Skill configuration errors.
6. Deployment
Skill management and deployment follows modern best practices. Skills can be stored in and loaded from any configured OCI registry.
7. Integration into PhariaAI
PhariaEngine integrates tightly into the PhariaAI stack. Traces can be viewed in PhariaStudio and Skills can be evaluated against uploaded datasets.
8. Discoverability
PhariaEngine makes it easy to retrieve descriptions and metadata like input and output schema for Skills.
9. Agent Ready
The more autonomy and control will be given to AI Skills, the more important it becomes that they run in a constrained environment in which every interaction with the outside world can be traced and maintained.
For this PhariaEngine already provides foundational building-blocks for AI agent readiness:
- All Skill executions generate traces for reviews.
- Enforces runtime limits (CPU/memory/time) to prevent overconsumption.
- Allocates resources dynamically on load & priority needs
- Support Tool Calling & CodeExecution of LLM generated code