How to build an analytics agent
Building an analytics agent is less about the model and more about the context you give it. The steps are connect your data, engineer the context, give the agent tools and a correction loop, evaluate it, and deploy. Skip the context and evaluation steps and you get a demo. Do them and you get something people can trust with a number.
Here is the order that works, and why each step matters.
1. Connect the warehouse
Start by giving the agent read access to your data, usually a warehouse like BigQuery, Snowflake, Postgres, or Databricks. This part is mostly plumbing. The one decision that matters is scope. Give the agent access to the tables it needs, not everything, so a wrong query cannot reach data it should not. The learn path covers the mechanics.
2. Engineer the context
This is the step that decides whether the agent works. A model pointed at a raw schema will guess, and on real data it guesses wrong. Context engineering is the work of giving it what the schema leaves out.
- Table and column descriptions, so “usr_st” is known to mean user status.
- A semantic layer or metric definitions, so “revenue” resolves to one agreed calculation instead of a guess.
- Business rules, like which table is canonical, which filters always apply, and what the agent should never do.
- Documentation of how the business actually works, so the agent has the same background a new analyst would get.
Treat this as files you version, not settings in a UI. It is the part you will keep improving, and it is where most of the accuracy comes from.
3. Give it tools and a loop
An agent is not a single call to a model. It is a loop. Give it the tools to do the job, like running a query, reading the schema, and looking up a definition. Then let it check its own work. When a query errors or returns something implausible, the agent should revise and retry rather than hand back the first attempt. This retry behavior, the agentic loop, is a big part of why agents beat plain text-to-SQL.
4. Evaluate before you trust it
Do not ship on vibes. Build a set of real questions with known answers and measure how many the agent gets right. Evaluating an analytics agent properly means checking the actual query results, not just whether the SQL looks reasonable, and running it as a regression test so a model or context change cannot quietly break accuracy. This is the step teams skip and the one that separates a real deployment from a risky one.
5. Deploy and keep improving
Once it clears your evals, put it where people work, whether that is a web app, Slack, or Teams. Then watch the questions it gets and the ones it gets wrong. Every wrong answer is a gap in the context, and fixing it is how the agent gets better over time. The learn path goes through rollout and access.
What you are really building
Notice what most of this is. Connecting the warehouse is plumbing. The model is mostly a given. The work, and the value, is in the context and the evaluation. That is why the teams who do this well treat the agent’s context as a product they maintain, not a prompt they wrote once. Open-source agents like nao are built around exactly this, giving you the structure to build, version, and test that context rather than starting from a blank prompt.
What do you need to build an analytics agent?
A data warehouse the agent can read, a language model, and, most importantly, context. That means table and metric definitions, business rules, and documentation that tell the model what the data means. You also need an evaluation set of real questions with known answers to measure accuracy before you deploy.
How long does it take to build an analytics agent?
Connecting a warehouse and getting a first answer can take an afternoon. Getting an agent accurate enough to trust takes longer, because that depends on engineering the context and building an evaluation set, which is ongoing work rather than a one-time setup. The context is what you keep improving.
Should you build or buy an analytics agent?
Adopt an existing agent for the loop, the tools, and the interface, since rebuilding those adds little. Then focus your effort on the part that is specific to you, which is the context and the evaluation. Open-source agents let you do this without rebuilding the engine, which is why many teams start there.