Anna vs Hex: workbench vs agent

Hex and Anna often come up in the same conversation, but they belong on different shelves. Hex is a workbench for analysts who write SQL and Python. Anna is the analyst.

That distinction is the whole essay. If your team already has someone who lives in notebooks, you don't need an agent that does the analysis for them — you need a better notebook. If your team doesn't have that person, a better notebook is the wrong shape; you need someone who can do the work end to end.

Both tools are good. They solve different problems for different people. Below, when each one fits — and why teams sometimes use both.

What Hex is built for

Hex is the best notebook environment going. SQL, Python, and visualisations live in a single cell pipeline, so an analyst can pull data, transform it, model it, and chart it without leaving the page. The cells stay live and reactive — change an input, downstream cells re-run.

The audience is clear: data analysts and data scientists at mid-market and enterprise companies. Hex assumes you can write SQL. It assumes you can read a Python traceback. It assumes you know what a window function does. Given those assumptions, it gives you a working environment that is genuinely excellent.

Warehouse integration is deep. Snowflake, Databricks, BigQuery, Postgres, Redshift — Hex talks to all of them as first-class citizens, with schema browsing, query history, and credential management built in. For a team standardised on a modern warehouse, Hex slots in cleanly.

Hex Magic is the AI layer. It helps the analyst write SQL faster, scaffold Python cells, explain a query, or suggest a chart. The framing matters: Magic is an assistant to the analyst, not a replacement for the analyst. It accelerates the human who's already at the keyboard.

Collaboration and governance are strong. Comments, version history, permissions, scheduled runs, published apps for stakeholders. A data team can run a real workflow inside Hex and trust it for production reporting.

The deliverable is the notebook. That's the shape Hex outputs — a cell-by-cell document that a technically literate reader can follow.

What Anna is built for

Anna is built for the operator who needs analysis but doesn't write code.

The audience is different by design: founders running a Series A company, marketing agencies servicing a dozen clients, social and content managers tracking what's working, brand managers, e-commerce operators. People who have the question and the context but not the SQL skills.

Live integrations are the connective tissue. Stripe for revenue. HubSpot for pipeline. GA4 for traffic. Klaviyo for email. Meta for ads. Social platforms for competitor and comment analysis. Warehouses too, when the team has one — but Anna doesn't require a warehouse to be useful. She reads from the operator's actual stack.

The output is a shareable report. URL you send to your CEO. Reading layout with charts, tables, and written commentary, plus an optional auto-generated PowerPoint for the board meeting. Not a notebook full of cells the recipient has to interpret — a finished document.

Anna runs the analysis end to end. Connects to the source, queries it, computes the math, writes up the findings with methodology shown, and ships the result. No SQL or Python required of the person asking the question. She'll explain how she got there if you want the detail; otherwise the headline answer is on top.

This is the agent posture. The human asks a question in plain language. The work happens. The answer arrives.

The workbench-vs-agent distinction

Take a concrete scenario: a Series A SaaS startup wants weekly cohort retention by signup channel.

With Hex, the path is familiar to any analyst. Connect to Postgres. Write the SQL to pull signups and activity events, joined by user. Write Python to build the cohort matrix — pandas pivot, percentage normalisation, indexing by signup month. Design the visualisation — a heatmap, probably, with cohort age across columns and signup month down rows. Review with stakeholders. Refine. Schedule the notebook to refresh weekly. Ship the link to leadership.

That workflow is good. The analyst owns it, the work is auditable, the notebook becomes a living artefact the team can iterate on.

With Anna, the path is shorter. The founder types "show me cohort retention since launch, broken out by signup channel." Anna queries the database, runs the cohort math, writes up the findings with the methodology visible, and shares a URL. The next week, the founder asks for an update — Anna refreshes against the live source.

Same job. Different actor. Hex makes an analyst more productive at the job; Anna removes the requirement to have an analyst doing the job.

This isn't bashing Hex. There's a real case for keeping an analyst in the loop — complex modelling, exploratory work where the question keeps changing, governance contexts where someone needs to sign off on the methodology, regulated industries where you need an auditable code trail. Hex is the right tool for those teams.

But if you don't have an analyst, a notebook environment is the wrong shape. You're not short a better workbench. You're short the person who knows how to use one.

What operators notice when they switch: the chart was already chosen for them — no axis fiddling, no chart-type menu — and the report lands ready to send. Analyst taste baked into every layout: what to highlight, what to call out, what belongs in the headline. Paste the URL into Slack the way you'd paste a Loom; the stakeholder gets the answer, not a notebook to interpret.

Dimension	Hex (workbench)	Anna (agent)
Who writes the SQL	The analyst	Anna does, behind the scenes
Output format	Notebook (cells)	Report (URL)
Time to first chart	Analyst time	Minutes
Who shares it	Analyst publishes	Anyone — paste the URL in Slack

How Anna combines model judgment with real math

There's one tactical capability that lives in Anna and doesn't have a real equivalent in Hex Magic — or in any notebook tool — yet.

Hex Magic helps an analyst write SQL or Python faster. Useful, but the LLM stays outside the data. It generates the code; the code processes the rows.

Anna's =AI() formula puts LLM judgment directly inside the data pipeline as a column transform.

Concrete example. You have 8,000 customer-support tickets. The question is whether sentiment correlates with retention. The first half of the analysis is per-row judgment: read each ticket, classify it as positive, negative, or neutral. The second half is deterministic math: join sentiment to retention outcomes, compute the rate per cohort, test the difference.

In Hex, an analyst writes a loop that calls the LLM row by row, handles retries, caches results, and writes a new column back to the dataset. It works. It's also plumbing the analyst now owns forever.

In Anna, that step is a formula: =AI("classify sentiment of {message}", "positive/negative/neutral"). Anna handles the batching, the retries, the caching, and the cost accounting. The next step — the cohort math — is real computation against the result. One workflow, both phases, no glue code.

LLM-as-column for judgment. Code-as-aggregator for math. That combination is what =AI() unlocks.

When teams use both

The honest answer is that some teams run both tools and they don't conflict.

A data team uses Hex for deep modelling, custom forecasting, and the production reporting the analyst owns. They use Anna for the operator-facing reports their internal stakeholders actually open — the marketing lead asking about campaign ROI, the CEO asking about pipeline health, the support manager asking about ticket trends.

A marketing agency uses Hex for client governance work that needs a documented methodology. They use Anna for the fast, repeatable reports — weekly competitor analysis, comment-trend reads on a client's recent launch, social-data summaries — that don't need a senior analyst's hours.

A founder uses Anna day to day through Series A. At Series B, they hire a head of data who brings Hex with them. Anna keeps serving the operator workflow; Hex serves the new data function. Both stay.

A note on intelligence

Hex Magic and Anna both use frontier LLMs. The intelligence is the same intelligence. What differs is where the LLM sits in the workflow — helping a human do the work, or doing the work — and what shape the output takes when it lands.

That's the choice you're making. Not which one is smarter. Which one fits the person who needs the answer.

Frequently asked questions

Does Anna replace our data team?

No. If you have an analyst who lives in Hex and ships notebooks your team trusts, keep both. Anna fills the gap for the operators around that analyst — the marketing lead, the founder, the support manager — who currently wait in a queue for a question that doesn't really need a senior analyst's time.

Can Anna write SQL like Hex Magic?

Anna writes and runs SQL when the analysis calls for it, against live integrations and warehouses. The difference is that you don't see the SQL unless you ask. The output is the report, not the code. For an operator that's the point; for an analyst auditing the work, the methodology and queries are available on request.

Why use Anna if I already have Hex?

For operator-facing reports your non-technical stakeholders actually read. For social-data and competitor analysis where the data lives outside the warehouse. For recurring questions across live integrations that don't justify another analyst hour. Some teams use both, and the boundary draws itself: Hex for the data team's work, Anna for everyone else's questions.

Is Anna a workspace?

Yes. Different shape from Hex's notebook workspace, though. Anna's workspace is organised around reports and projects — the artefacts an operator ships and shares — not around notebook cells. The unit of work is the answered question, with the data, the analysis, and the writeup attached.