Skip to content

Cake for
Agentic RAG

Move from prototype to production faster with pre-vetted configurations of the latest open-source tools. Build dynamic, multi-step agent workflows that retrieve, interpret, and act on enterprise data, with orchestration, prompt optimization, model routing, and full-stack observability on a cloud-agnostic, modular stack.

 

agentic-rag-a-complete-guide-for-business-846635
Customer Logo-4
Customer Logo-1
Customer Logo-5
Customer Logo-2
Customer Logo

Overview

Retrieval-augmented generation (RAG) gives AI the ability to pull in live, relevant information from your enterprise knowledge before generating a response. Instead of relying only on static training data, RAG-powered agents can answer with accuracy, context, and up-to-the-minute insight. This is critical for tasks like customer support, research, and decision-making.

But building agentic RAG systems is notoriously complex. From vector search and orchestration to observability and fine-tuned access control, each layer introduces new integration challenges. Most teams spend weeks (if not months) just stitching components together, delaying launches and driving up costs.

Cake changes that. With pre-validated, production-ready configurations of the latest open-source tools, AI developers can go from prototype to production in days. You keep your own models, prompts, and vector stores. Cake delivers the glue logic, observability, and security scaffolding so you can move fast without cutting corners.

Key benefits

  • Ship faster: Go from notebook to deployed agentic system without gluing together infrastructure.

  • Use open-source tooling: Mix and match LLMs, routers, and chunkers without vendor constraints.

  • Scale securely: Build workflows that grow with your data, teams, and compliance needs.

  • Debug with visibility: Track agent behavior, data flow, and model decisions with full observability.

  • Build on a proven RAG foundation: Start from pre-vetted configurations of cutting-edge tools, reducing setup time and avoiding costly integration pitfalls.

Group 10 (1)

Increase in
MLOps productivity

 

Group 11

Faster model deployment
to production

 

Group 12

Annual savings per
LLM project

HOW IT WORKS

Thinline

 

Accelerate to production with

Cake's pre-built configurations

  • Connect your data: Ingest unstructured documents from sources like S3 or local drives using Cake’s prebuilt ingestion pipelines.
  • Spin up intelligent agents: Build multi-step RAG workflows with LangGraph and Langflow, powered by your own vector store and orchestrated through a simple web UI.
  • Optimize your prompts: Use DSPy for prompt generation and Promptfoo for automated evaluation that's 100% pre-integrated and configurable.
  • Serve and route models: Run inference through LiteLLM and vLLM, with built-in model tracking and fine-tuning via MLflow.

HOW IT WORKS

Thinline

 

From lookup answers to agents

that actually get work done

vendor-approach-icon

Naive RAG

Good for demos, brittle in production: A basic retrieval-augmented generation loop with a static prompt and shallow retrieval.

  • Relies on simple query-to-response flow
  • Single-turn generation without memory or reasoning
  • Weak retrieval logic often returns irrelevant chunks
  • Hard to scale, evaluate, or debug

Result:

Works in a notebook, fails in the real world

cake-approach-icon

Agentic RAG with Cake

Built for reasoning, context, and multi-step tasks: Use Cake to deploy agents that retrieve, plan, and adapt in real time.

  • Agents combine RAG with tools, workflows, and memory
  • Supports multi-turn, multi-source reasoning
  • Built-in evals, tracing, and observability
  • Easily extend with function calling, vector filtering, and reranking

Result:

Scalable, production-grade AI agents that actually deliver value

EXAMPLE USE CASES

Thinline

 

Turn data into action with Agentic RAG

robot (1)

Intelligent support agents

Retrieve relevant context, route queries across tools, and provide multi-turn assistance grounded in real data.

person-with-a-robot-looking-at-a-book

Research copilots

Use long-context models to iteratively retrieve, read, and synthesize information across multiple queries.

gear

Enterprise task automation

Enable agents to retrieve internal data, reason over it, and take action using custom toolchains.

paper-coming-out-of-machine

Autonomous report generation

Build agents that can retrieve internal data, run analysis, and compile human-readable reports without manual prompting.

sequence

Multi-step reasoning across tools

Enable agents to sequence multiple retrieval and execution steps, like querying a vector DB, calling a function, then refining the answer.

lock

Secure RAG for regulated environments

Deploy retrieval workflows that respect row-level permissions, data residency rules, and auditability requirements.

VIDEO

What it really takes to productionize RAG

During his presentation at Data Council 2025, Cake co-founder & CTO Skyler Thomas breaks down what it takes to move RAG from prototype to production in enterprise environments, including:
  • Why most RAG projects stall before reaching production
  • How to structure evaluation, orchestration, and observability
  • The open-source patterns behind scalable agentic systems
  • How Cake simplifies the path to enterprise-grade deployment

 

BLOG

Why 90% of Agentic RAG Projects Fail (and How Cake Changes That)

Most teams get stuck in the “60% demo trap,” i.e., a prototype that works in a notebook but breaks in production. This blog breaks down where naive RAG architectures fall short and how Cake helps you build agentic systems that are observable, scalable, and goal-driven.

Read More >

testimonial-bg
“

"Our partnership with Cake has been a clear strategic choice – we're achieving the impact of two to three technical hires with the equivalent investment of half an FTE."

Customer Logo-4

Scott Stafford
Chief Enterprise Architect at Ping

Read The Case Study
testimonial-bg
“

"With Cake we are conservatively saving at least half a million dollars purely on headcount."

CEO
InsureTech Company

Read the case study
testimonial-bg
“

"Cake powers our complex, highly scaled AI infrastructure. Their platform accelerates our model development and deployment both on-prem and in the cloud"

Customer Logo-1

Felix Baldauf-Lenschen
CEO and Founder

COMPONENTS

Thinline

 

Tools that power Cake's Agentic RAG stack

Frequently asked questions

What makes Cake ideal for building Agentic RAG systems?

Cake gives you a production-ready, cloud-agnostic stack that supports long-context LLMs, vector databases, chunkers, routers, and agent frameworks sans the glue code. You get observability, orchestration, and evaluation out of the box.

How does Cake speed up Agentic RAG development?

Can I use my preferred LLMs, retrievers, and agent frameworks with Cake?

How does Cake help me scale Agentic RAG securely?

What kind of observability does Cake provide for Agentic RAG?

Learn more about Agentic RAG

Best open-source tools for agentic RAG.

Best Open-Source Tools for Agentic RAG

Think about the difference between a smart speaker that can tell you the weather and a personal assistant who can check the forecast, see a storm is...

Vector Databases illustration

Top 10 Vector Databases: Choosing the Right One for Your Project

Key takeaways Vector databases are essential infrastructure for modern AI: They power search, memory, and retrieval across RAG, agentic, and...

How to Build Agentic Rag illustration

How to Build an Agentic RAG Application

Your business has data everywhere—in documents, databases, and real-time streams. A standard RAG system can help you find answers within that data....