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From AI ideas to working agents in production

Pronto-Sage helps organizations design, test, and run AI agents and agentic workflows - with a visual SaaS platform, expert development services, and hands-on training for your team.

  • Build and configure agents in Pronto-Studio, our main product surface.

  • Connect workflows to your existing systems using built-in tools for SQL, Elasticsearch, Qdrant, web search, SMTP email, and more.

  • Choose how you work: use the platform directly, upskill your team with Pronto-Sage Academy through hands-on, observable exercises, or collaborate with our team on complex use cases.

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Where Pronto-Sage is heading

We design Pronto-Sage with a long-term view: stable foundations, measured feature growth, and a clear path from first prototype to sustainable operations. No hype, just deliberate evolution.

Growing library of workflow templates

We steadily add more ready-to-use workflow templates for assistants, retrieval-augmented generation (RAG), analytics agents, operational automation, and risk/compliance use cases — so teams can start from working examples instead of a blank canvas.

Broader catalog of tools and integrations

Beyond the current tools for SQL databases, Elasticsearch, Qdrant-style vector stores, web search, and outbound email, we plan to expand the catalog with additional data sources, connectors, and utility tools that remain easy to configure inside Studio.

Deeper operational guardrails

We are continually improving how teams observe, test, and iterate on agents in production: clearer workflows for validation, more structured telemetry, and better support for controlled rollout and change management.

More learning paths in Academy

Academy will continue to add practical courses and lab exercises, from foundations of agentic design to advanced operational practices, always built around real projects in Pronto-Studio.

(No dates or guarantees; we focus on sustainable, production-grade capabilities rather than flashy one-off features.)

What You Build in Pronto Studio

How the Pronto-Sage platform is built

You don’t need a research lab to use Pronto-Sage. The platform bridges the skills gap and automates key steps in developing and deploying AI agents, while remaining use-case agnostic. You define the problem; Pronto-Studio lets you construct the workflow and use production-like PoC runs and observability to see early where the design needs adjustment.

01

Pronto-Studio: the main workspace

Pronto-Studio is your visual control plane. You sketch workflows on a canvas, define inputs and outputs, configure tools and data sources, and run tests — without writing everything from scratch.

02

Built-in orchestrator inside Studio

Every workflow you design in Studio is executed by the built-in Pronto-Engine. It lives inside the platform as the main orchestrator and is not sold as a separate product. It turns your diagrams and configurations into reliable, repeatable executions.

03

Workflows as microservices

Each Studio workflow has a clear input and output schema. When you are ready, it can be exposed as a Web API and treated like a small, focused microservice that other systems can call.

04

Connectors and tools, not custom glue

Instead of writing ad-hoc scripts, you configure built-in tools for things like SQL databases, Elasticsearch clusters, Qdrant-style vector search, web search, and email sending. Agents call these tools as part of the workflow.

05

Knowledge assets, not black boxes

Knowledge Bases and external systems are wired in explicitly. You always see which agents use which data sources and tools, so there are fewer surprises and easier approvals from security and compliance teams.

06

One model of “how agents work”

Whether you plug in a hosted LLM, a cloud provider, or a self-managed model endpoint, the workflow model in Studio stays consistent. You design once and can adjust providers as needed.

Design & ModelsIntegration & ToolsDeployment & API

From idea to production, with built-in accelerators

Pronto-Sage is not just a blank canvas. It comes with starting points, examples, and training that reduce the time between “we have an idea” and “this is running in production”.

Prebuilt workflow templates

Start from ready-made templates for common patterns instead of reinventing the wheel:

  • RAG / knowledge-based Q&A assistants
  • Internal data analytics agents
  • Risk and compliance analysis workflows
  • Operational assistants for support, HR, and finance

Each template is a full Studio workflow you can open, inspect, and adapt.

Example projects and reference designs

Explore example workflows that illustrate good practices for structuring prompts, chaining tools, handling errors, or combining multiple agents. They’re meant to be read, modified, and re-used — not hidden behind proprietary abstraction.

Pronto-Sage Academy

Academy provides structured teaching around the same platform. Teams learn how to:

  • Think in terms of agents, tools, and responsibilities
  • Design workflows that are testable and auditable
  • Move from quick demo to maintainable, production-ready agents

Guided engagements when needed

For organizations that want extra support, Pronto-Sage can run focused engagements to help design and validate workflows — using the same platform you’ll later own and operate.

Strategic Advantage

Why organizations choose Pronto-Sage

There are many ways to build with AI. Pronto-Sage focuses on the gap between “we can prompt a model” and “we can reliably run agents in production”.

Avoid the “PoC trap”

Many teams get stuck in prototype mode: impressive demos that never become dependable systems. Pronto-Studio is built around clear inputs/outputs, test runs, and deployment pathways, so the same workflow can move from experiment to production without starting over.

Empower more than just specialists

With Studio, people who understand the business problem — not just those who write Python all day — can help model workflows, data flows, and tool usage. Technical teams still keep control, but they’re not the only ones who can contribute.

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Built for real systems, not lab demos

Workflows integrate with your databases, search indices, vector stores, and communication tools. You can plug into existing SQL, Elasticsearch, Qdrant-style stores, web search engines, and email infrastructure rather than copying data around.

Control over deployment

When you’re ready, you decide where workflows run: As API endpoints in a Pronto-Sage–managed environment, or As container images in your own infrastructure. Either way, the behavior is defined by what you configured in Studio.

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Platform + services + education

You can adopt the platform and keep full ownership, bring in Pronto-Sage services for specific projects, and use Academy to build internal capability over time. You’re not locked into a single “we do everything for you” model.

Engineering Principles

Technology approach in plain language

Under the hood, Pronto-Sage follows a few straightforward engineering principles.

Agentic workflows, not one-off prompts

Agents are organized into workflows: a series of steps with clear responsibilities, inputs, and outputs, rather than a single “magic prompt” that’s hard to reason about.

Pluggable language models

The platform is designed so you can connect to different LLM providers or your own model endpoints, while keeping a stable workflow structure in Studio.

Tool-first integrations

External systems — SQL databases, Elasticsearch, Qdrant-style vector stores, web search, email — are exposed as configurable tools. Agents call these tools through the orchestrator instead of embedding brittle logic in prompts.

Flexible state and memory

For each workflow and agent, you can decide how runtime state is handled: keep runs stateless, enable short-lived “memory” during tests, or persist context in a database. You can use the managed storage provided by the platform or connect workflows directly to your own databases for long-lived state.

Workflows as APIs and containers

When a workflow is ready, it can be exposed as a Web API in a Pronto-Sage–managed environment or packaged as a container image you can deploy on your own infrastructure.

Observability as a core concern

Workflows are designed with traceability in mind, so teams can see what happened during a run, debug issues, and improve behavior over time.

Core Use Cases

From Design to Deployment

Design in Pronto Studio → validate as PoC → deploy as a self-contained microservice via Web API. Real workflows solving real problems.

Customer Support Workflows
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Customer Support Workflows

STATUS: TRIAGE_ACTIVE

Triage → retrieve context → propose resolution → handoff or execute. Design in Pronto Studio, validate as PoC, deploy as a self-contained microservice via Web API.

Document Workflows

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Research & Analysis Flows

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Frequently Asked Questions

Operational FAQ

Understanding Pronto-Sage's approach to deployment, integration, and the journey from PoC to production.

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You can run workflows as API endpoints in a Pronto-Sage–managed environment, or export them as container images and deploy them on your own infrastructure - from a single server to Kubernetes or other orchestration platforms. In both cases, the behavior and Web API contract stay the same.

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Next steps with Pronto-Sage

Whether you’re exploring your first AI agent, scaling beyond early PoCs, or building an internal capability for agentic systems, Pronto-Sage is designed to meet you where you are — with a platform, services, and education that fit together.

(We’ll discuss your use cases, existing systems, and constraints, and suggest a concrete way to start — from a small pilot to a broader rollout.)

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