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The Complete Agent Stack

The Complete Agent Stack

The Complete Agent Stack

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A 14-part guide to what a data agent needs before people can rely on its answers, from the data model to the agent that fixes your agent.

Ka Ling Wu

Co-Founder & CEO, Upsolve AI

10 min

Dash, the Upsolve narwhal, building a wall of coloured blocks labelled data model, knowledge base, skills, prompts and memories

The Complete Agent Stack

A 14-part guide to what a data agent needs before people can rely on its answers.

Most data agents work in the demo and start failing within a couple of months. The model rarely changes in that time. The business, the data and the definitions around it do, and an agent without the right context has no way to keep up.

The complete agent stack is how we think about building agents that last. It has four layers, and each guide in this series covers one feature in one layer. Read it from the foundation up, or go straight to the layer you're working on.

Diagram of The Complete Agent Stack: ontology, agent harness, distribution and improvement loop layers holding 14 features

Each layer builds on the one below it, and the improvement loop feeds back into the ontology.

Layer 1: Ontology

Your data described clearly, with permissions enforced outside the agent.

  1. AI Agent Data Model: How to Give Every Answer One Definition

  2. Row-Level Security for AI Agents: Permissions a Prompt Can't Override

  3. MCP vs SQL for Data Agents: Why Cross-Source Analytics Needs Real Joins

  4. AI Agent Context Versioning: How to Ship an Agent as One Release

  5. How to Set Up a Data Agent: Eight Steps From Raw Tables to Trusted Answers

Layer 2: Agent harness

The engine that turns context into answers and learns from every user.

  1. AI Agent Harness: How Skills and Memory Make Data Agents Reliable

  2. MCP Servers for Data Agents: How to Add Context and Actions Safely

Layer 3: Distribution

Answers delivered where your users already work.

  1. AI-Generated Dashboards: How to Turn Chat Answers Into Shared BI

  2. AI Report Builder: How to Turn Dashboards Into Branded Data Apps

  3. Claude MCP for Data Analysis: How to Get Governed Answers in Claude

  4. How to Evaluate an AI Data Agent: A Buyer's Checklist Before You Commit

Layer 4: Improvement loop

Live evals on real usage find the gaps, and you harden the context to close them.

  1. AI Agent Observability: How to Catch Wrong Answers in Production

  2. Golden Questions for AI Agent Evals: Test Cases and Confidence Scores

  3. Data Drift in AI Agents: Why Data Agents Decay After Launch

Try the stack

Every feature in this series is available on the free tier, with 2,000 AI credits. Start free at upsolve.ai, or talk to our team.

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