Overview: Why PhariaEngine?

Frameworks like llama-stack and langchain allow for quick prototyping of generative AI applications. However, transitioning prototypes to production requires solving different challenges regarding:

  • robust security

  • testability

  • traceability

  • evaluation of the AI logic

PhariaEngine can help you evolve your proofs of concept by allowing you to develop with production in mind from the beginning.


What problems does PhariaEngine solve?

Many companies that experiment in AI methodology tend to wrap a Python-based framework into isolated containers with custom web servers. While this does work, it creates redundancies:

  • It reinvents foundational components, such as authentication, tracing, and scaling.

  • It creates unique dependencies per container requiring constant updates and synchronization.

  • It scales inefficiently because of redundant and divided resource allocation.

These are the problems that PhariaEngine solves. By providing a unified runtime environment, PhariaEngine eliminates redundancy and provides a constrained yet capable interface that each Skill can interact with. It provides the following benefits:

  • It unifies foundational components — authentication, tracing, scaling — into a single runtime environment.

  • It simplifies deployment using OCI registries and modern CI/CD practices.

  • It allows developers to focus on the methodology development of the business logic.

From prototype to production in one secure platform

It is fairly straightforward to construct proof-of-concept AI Skills. However, these projects are often a security risk, they lack scalability and, in short, they risk becoming a maintenance nightmare.

PhariaEngine redefines production-grade AI deployment by allowing for rapid prototyping and immediate transformation into secure, scalable, and maintainable AI Skills.

With PhariaAI, you can deploy hundreds of AI Skills with the following advantages:

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.

Automatic scaling of resources

Skills in PhariaEngine run as serverless components, meaning your code can serve many requests in parallel. Blocking calls to inference (for example, due to length), which can become a problem in other AI applications, do not 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 allocates resources dynamically between Skills.

Minimal-dependency maintenance

PhariaEngine provides built-in implementations for common protocols (such as HTTP and gRPC) and file standards, minimising third-party dependencies. Fewer dependencies result in a smaller maintenance burden to keep dependencies up-to-date.

Built-in security

AI applications are most useful when they have access to your knowledge base. But this also implies a potential security risk because this knowledge must not be exposed to unauthorised parties.

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 minimising the attack surface.

Built-in 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.

Skills management

Skills management and deployment follow modern best practices. Skills can be stored in and loaded from any configured OCI registry.

Seamless integration into PhariaAI

PhariaEngine integrates tightly into the PhariaAI platform. Traces can be viewed in PhariaStudio and Skills can be evaluated against uploaded datasets.

Discoverability

PhariaEngine makes it easy to retrieve descriptions and metadata like input and output schema for Skills.

Agent-ready infrastructure

The more autonomy and control given to AI Skills, the more important it is that they run in a constrained environment in which every interaction with the outside world can be traced and maintained.

To this end, PhariaEngine already provides foundational building-blocks for AI agent readiness:

  • All Skill executions generate traces for reviews.

  • Runtime limits (such as CPU, memory, and time) are enforced to prevent overconsumption.

  • Resources are dynamically allocated based on load and priority requirements.

  • Tool calling and code execution of LLM generated code is supported.