For AI agents, context is everything. An LLM can write syntactically perfect SQL all day long, but without explicit business definitions, it will still deliver incorrect answers with complete confidence.
Amazon Athena is a good test case for this problem. Amazon Athena gives you serverless SQL access to data sitting in Amazon S3. That access has made it a default choice for teams running fast, on-demand queries without managing infrastructure.
Connecting an agent directly to Amazon S3 through Athena provides performance for data from that source, but it requires data centralization for data held in other sources. When you point an AI agent at Athena, serverless query access alone doesn’t tell the agent what a customer, a region, or a churn event means across your business. Solving this gap requires shifting focus from prompt engineering to context engineering, creating a governed semantic layer that equips agents with true organizational meaning before a single query executes.
This article looks at why Athena’s query model wasn’t built for agentic AI or agent grounding, what a governed context layer adds on top of it, and how you can start building that layer without moving off Athena or replacing what already works.
Key takeaways
- Athena gives AI agents serverless SQL access to Amazon S3 data, but not the governed business context they need to answer correctly and safely.
- Context engineering is a process that creates a governed layer of business definitions, metadata, and access policy that provides AI agents with the raw Athena access they need.
- Most AI agent projects stall due to lack of data and context access rather than model quality, and recent estimates put agent-project production deployment at only 30 percent.
- A federated context layer lets agents query Athena alongside Snowflake, Redshift, and other sources while limiting the need to copy the underlying data.
- Governance, lineage, access policy, and shared definitions have to be engineered into the context layer up front, not added after agents are already querying Athena.
What context engineering means for an Athena environment
From prompt engineering to context engineering
For the past few years, most AI work focused on prompt engineering, wording a request so a model responds well. Context engineering asks instead what business definitions, metadata, and access rules does an agent need before it ever sees a prompt. With context engineering, you build a governed layer of shared definitions and metadata that sits between your data and any model you connect to it. As models commoditize, the context layer becomes what separates an agent that gives correct answers from one that guesses.
Why Athena’s serverless model isn’t built for agent grounding
Athena isn’t built for this. It runs SQL against Amazon S3 data on demand, and you pay per query scan. That model works well for ad hoc analysis, but it wasn’t designed to carry business meaning at the contextual level. Athena has no built-in concept of a shared metric definition, a data product boundary, or a policy that travels with a table wherever it’s queried. When an agent queries Athena directly, it sees columns and rows, not what those columns mean to your finance or product team. Agent grounding closes that distance by giving agents a consistent, governed reference for what the data represents before they answer a question.
The context gap Athena leaves for AI agents
SQL access isn’t the same as business meaning
Give an agent a valid Athena connection, and it can write and run correct SQL. That’s a query problem, and Athena solves it well. The harder problem is a semantic one, and it’s much harder to solve because it requires knowledge of context.
For example, does “active customer” mean the same thing in your Athena tables as it does in your CRM, and will the agent apply that definition consistently? Without a governed layer, two agents can query the same Athena table and land on two different answers to the same question, each technically correct and neither usable for a decision.
Cost, performance, and governance limits compound the gap
There are other considerations as well. Athena’s pay-per-scan pricing penalizes exploratory queries, which is exactly the pattern agents produce when they ask several follow-up questions to reason through a problem. Multiple agents scanning the same partitions can also create noisy-neighbor contention that slows every other user on the account.
None of this comes with a governance framework built in. There’s no lineage, no consistent access policy, and no audit trail that shows why an agent reached a conclusion. Each of those has to be engineered in, not assumed.
What an enterprise context layer adds on top of Athena
Federated access solves this problem. It provides a universal point of access, allowing for universal data access. This approach is perfect for AI agents that require access to a contextual layer because it allows access to that context, regardless of where it lives, without requiring data centralization to achieve it.
Let’s look at how this works in practice.
Federated access across Athena, Snowflake, Redshift, and other sources
In production, an enterprise context layer sits above Athena and your other systems, connecting to Snowflake, Redshift, and additional sources so an agent can query across all of them while limiting the need to copy data into a new store. The context layer combines that federated access using shared data products, so an agent asking about revenue by region gets one governed answer whether the underlying rows live in Athena or somewhere else.
Data products as agent-consumable context
A context layer works because it packages data into data products that an agent can trust. Each one carries a definition, machine-readable metadata, and version history instead of column names alone. This creates a consistent, versioned interface, so a model doesn’t need to guess what a raw Athena table means or infer a business rule from column names alone.
Governance, lineage, and policy enforcement
Access policy, lineage, and shared definitions need to be built into the context layer from the start, not added after agents are already running queries against Athena. That includes tracking agentic data, the reasoning logs and tool calls agents themselves generate, under the same governance rules as the data they queried to produce it. Additionally, every policy enforced at this layer applies once, centrally, instead of being rebuilt separately for Athena, Snowflake, and every other source an agent might touch.
How context engineering changes what agents can do with Athena data
From natural-language SQL to grounded, explainable answers
With a context layer in place, an agent can take a natural-language question, translate it into SQL using Athena as a data source, combine it with data from another source, and return an answer tied to a governed definition instead of a guess. You can trace that answer back to the data product, the definition, and the underlying rows that produced it, and that traceability is what makes the answer explainable rather than a black box.
Building a context engineering roadmap for an existing Athena data estate
How do you create this workflow in production? Starburst is designed to provide access to your context layer, and combine that data alongside data from your entire organization using data federation to provide universal data access.
Here’s how that works in practice.
Audit current Athena workloads and where context is missing
Start by listing which Athena tables and workloads any agent project would touch, then check each one against a simple question:
- Does a shared, documented definition already exist for the fields an agent would need?
Most teams find the missing piece isn’t the SQL. It’s the definition layer. That finding lines up with the data. Recent estimates put agent-project production deployment at just over 30 percent, up from a reported 88 percent of AI projects that never reached production in 2025, largely because of enterprise AI data access problems rather than model quality.
Add a context layer without a rip-and-replace migration
None of this needs you to migrate off Athena or consolidate every source into one platform. A context layer connects to Athena where it already runs, adds governed data products and shared definitions on top, and extends the same policy to Snowflake, Redshift, and other sources as you add them. You can also expose that layer to agents through a governed, multi-tenant hosted MCP service, so tools and context reach agents through one controlled path instead of a separate integration for every source. That’s your AI data strategy built on what you already run, not a replacement for it.
Want to know more about accessing Athena alongside your business context using Starburst? Download the Data Engineer’s Guide to Reducing Your Cloud Data Warehouse Spending.
FAQs
What is context engineering, and how is it different from prompt engineering?
Prompt engineering focuses on wording a request so a model responds well. Context engineering asks what business definitions, metadata, and access rules an agent needs before it ever sees a prompt. You build a governed layer of shared definitions and metadata that sits between your data and any model you connect to it, so accuracy depends on that layer instead of on model choice alone.
What’s missing when agents query Amazon Athena directly?
Give an agent a valid connection to Amazon Athena and it can write and run correct SQL. That solves the query problem, not the semantic one. Athena has no built-in concept of a shared metric definition or a policy that travels with a table, so two agents can query the same table and land on two different, technically correct answers to the same question.
Do you need to migrate off Athena to add a context layer?
No. A context layer connects to Athena where it already runs and adds governed data products and shared definitions on top of it. You extend that same policy to Snowflake, Redshift, and other sources as you add them, building your AI data strategy on what you already run instead of a rip-and-replace migration.
How does an enterprise context layer relate to a semantic layer?
An enterprise context layer sits above Athena and your other systems, combining federated access with data products that carry a metric definition, machine-readable metadata, and version history. It extends what a semantic layer does by making those shared definitions consumable by agents as well as BI tools, so an agent asking about revenue by region gets one governed answer no matter which system holds the rows.
What governance controls does a context layer add?
A context layer adds lineage, a consistent access policy, and an audit trail that shows why an agent reached a conclusion, none of which Athena provides on its own. It also tracks agentic data, the reasoning logs and tool calls agents generate, under the same governance rules as the data they queried to produce it, so every policy applies once, centrally, instead of being rebuilt for each source.
