Create AI agents for internal operations in Slack

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January 20, 2025

Inferable

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Create AI agents for internal operations in Slack

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Inferable is an open-source platform designed for fast creation of AI agents using existing codebases, APIs, and data. The platform is explicitly useful for consolidating internal systems and fragmented codebases via conversational agents, improving internal processes and tooling operations.It provides a comprehensive agent runtime, incorporating vertically integrated agent orchestration to bring product and domain expertise together.

The Inferable architecture revolves around a distributed message queue ensuring reliable and scalable AI automations. Developers experience convenience due to the system's facility to decorate existing functions and APIs, eliminating the need for learning new frameworks.

Inferable supports various coding languages including Node.js, Golang, and C#, and plans to include more in the future. One notable feature is Human-in-the-Loop modeling, which gives room for a function execution to be paused for an unpredictable amount of time, thereby seamlessly integrating human responses into the workflows.

A noteworthy feature of Inferable is on-premise execution, which ensures that your functions exclusively run on your infrastructure, posing limitations to the actions your functions allow.

This feature keeps the system secure by eliminating the need for incoming connections or load balancers.In terms of observability, Inferable provides end-to-end visibility into AI workflows and function calls without any configuration requirements.

It also enforces structured outputs, and facilitates the composition, piping, and chaining of outputs using language primitives. Lastly, Inferable is enterprise-ready from the beginning, easily adapting to existing architectures, allowing self-hosting, and offering a managed cloud with auto-scaling and high availability.

It can be self-hosted for complete control over data and compute.

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September 18, 2024

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Pros and Cons

Pros

Open-source platform

Uses existing codebases

Leverages APIs and data

Consolidates internal systems

Improves internal processes

Tooling operations enhancement

Agent runtime provided

Vertically integrated agent orchestration

Distributed message queue

Decorates existing functions and APIs

Supports Node.js, Golang, C#

Plans for more language support

Human-in-the-Loop modeling

On-premise execution

System security enhancement

End-to-end visibility

Function call observation

Structured outputs

Enterprise-ready

Adapts to existing architectures

Allows self-hosting

Managed cloud service

Auto-scaling

High availability

Agent orchestration off-the-shelf

Pause and resume executions

Full workflow observability

No configuration required

Composition, piping, chaining of outputs

Language primitives use

Codebase reuse without new frameworks

Slack integration for internal operations

Offers SDKs for fast production

Built-in ReAct agent

Self-hostability on own infrastructure

Data and compute control

Supports private networking

Run schema for output enforcement

Function-call observability

BYO model support

Structured output enforcement

Supports on-premise execution

No need for incoming connections

Easy-to-decorate functions and APIs

View 39 more pros

Cons

Limited language support

On-premise execution limitations

Indeterminate execution pause time

No dynamic output flexibility

Requires existing codebases/apis

Structured outputs only

Dependence on message queue

Potential compatibility issues

Own infrastructure requirements

Lacks short term user feedback

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Q&A

What is Inferable and how does it work?

Inferable is an open-source platform that enables quick creation of AI agents through the utilization of existing codebases, APIs, and data. It works by decorating existing functions and APIs so developers don't have to learn new ones. Through its vertically integrated agent orchestration, expertise across products and domains are easily bridged. Inferable runs on a distributed message queue, ensuring reliable and scalable AI automations.

What are the key features of Inferable?

Inferable offers several key features: a simple-to-use interface for creating AI agents, support for various coding languages (Node.js, Golang, and C#), Human-in-the-Loop modeling, on-premise execution for greater security, full visibility into AI workflows and function calls, enforcement of structured outputs, and readiness for enterprise use.

How can Inferable help in consolidating internal systems and fragmented codebases?

Inferable enables the consolidation of internal systems and fragmented codebases by developing conversational agents that help integrate different codebases and systems into coherent workflows. This streamlines processes and improves operational efficiency.

How does Inferable incorporate vertically integrated agent orchestration?

Inferable incorporates vertically integrated agent orchestration by providing an agent runtime that allows for easy synchronization and management of AI agents. It brings together product and domain expertise, ensuring seamless operation in a scalable and reliable manner. At its core, Inferable operates on a distributed message queue with at-least-once delivery guarantees.

What languages does Inferable support?

Inferable currently supports several popular coding languages including Node.js, Golang, and C#. It offers first-class support that helps streamline and improve the development process.

What is Inferable's plan for future language support?

Inferable has plans to expand its language support in the future. While there are no specific details available regarding which languages it plans to support next, it's clear that they're committed to being inclusive of a wide array of coding languages.

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Can you explain the Human-in-the-Loop modeling feature of Inferable?

Inferable's Human-in-the-Loop modeling feature allows for a function execution to be paused for an unpredictable amount of time. This facilitates seamless integration of human responses into workflows, ensuring that whether the human responds within minutes or months, execution will pause and resume fluidly.

What benefits does on-premise execution provide?

On-premise execution ensures that your functions run exclusively on your infrastructure. This gives you full control of your data and compute, which enhances security and allows for customization as per your specific requirements.

How does Inferable ensure system security?

Inferable ensures system security by enabling on-premise execution of functions, thereby eliminating the need for incoming connections or load balancers. Functions run on the user’s own infrastructure, and therefore can't perform actions they're not allowed to.

Can you describe the visibility Inferable provides into AI workflows and function calls?

Inferable provides end-to-end visibility into AI workflows and function calls. This observability doesn't require any configuration and enables you to monitor and manage the performance and results of your AI agents in a seamless way.

How does Inferable enforce structured outputs?

Inferable enforces structured outputs by ensuring that outputs meet specific formats and standards. It also allows for the composition, piping, and chaining of outputs using language primitives, to ensure the outputs are usable and functional.

What makes Inferable enterprise-ready?

Inferable is enterprise-ready as it adapts seamlessly to existing architectures, facilitating easy incorporation into current systems. It can be self-hosted for complete control over data and compute, and it also offers a managed cloud with auto-scaling and high availability for enhanced scalability and reliability.

Can Inferable be self-hosted and what are the benefits?

Yes, Inferable can be self-hosted, allowing complete control over your data and compute. Self-hosting offers enhanced security, customization, and control of your resources.

How does Inferable's managed cloud feature work?

Inferable's managed cloud feature entails a comprehensive cloud-based solution with auto-scaling and high availability. This ensures optimal performance and reliability of your AI agents, accommodating increased demand and ensuring seamless operation.

What does it mean that Inferable is an open-source platform?

Being an open-source platform, Inferable is publicly accessible and can be utilized, modified, and distributed by anyone. This increases transparency, promotes community-led development, and means improvements and features can be added by the community at large.

How does Inferable's system facilitate fast creation of AI agents?

Inferable facilitates fast creation of AI agents by providing an interface designed specifically for this purpose. Through support for various common coding languages (Node.js, Golang, and C#) and an ability to integrate existing codebases and APIs, it enables developers to create AI agents rapidly and efficiently.

Does the on-premise execution feature pose any limitations?

The on-premise execution feature of Inferable ensures that your functions exclusively run on your infrastructure, thereby posing restrictions to the actions your functions can perform. This setup enhances security by eliminating the necessity for incoming connections or load balancers.

In what ways can Inferable improve internal processes and tooling operations?

Inferable can improve internal processes and tooling operations by consolidating internal systems and fragmented codebases through conversational agents. This results in streamlined workflows and increased operational efficiency.

How does Inferable integrate with Slack for internal operations?

Inferable integrates with Slack to develop AI agents for internal operations. This allows for the consolidation of internal systems and fragmented codebases with the support of conversational agents, making it more efficient to manage internal tools and processes.

Can developers use their existing functions and APIs with Inferable?

Yes, developers can decorate their existing functions and APIs with Inferable. This way, there is no need to learn new frameworks, and it encourages the reuse of existing assets, which expedites the AI agent creation process.

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