Near-100% Accurate Data for your Agent with Comprehensive Context Engineering
To understand why agentic workflows require near-100% accuracy on questions that are answerable by your database data, let’s look at the numbers: Assume an accuracy of 90% in a single-step AI process. You ask a question; you get a correct answer 90% of the time. But in an agentic workflow, the AI takes multiple dependent steps – and errors compound exponentially.
Let’s run the numbers on a 90% accurate agent:
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One step: 90% success rate.
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Two steps: 0.90 × 0.90 = 81% success rate.
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Five steps: 0.90^5 = 59% success rate.
Now, imagine that same five-step workflow running on an 80% accurate agent. The success rate plummets to just 33%.
In a business context, even 90% accuracy is often insufficient. And 59% or 33% success rate is downright catastrophic. Indeed, in many industries near-100% accuracy is needed, because the agentic application is customer-facing and inaccuracies lead to loss of trust and loss of revenue. Furthermore, in many industries there are legal, safety and compliance requirements. In such industries, near-100% accuracy must be combined with explainability so that the human-in-the-loop can understand and verify the answers.
Example: consider a real estate agency using an AI workflow to handle new tenant onboarding in a five-step flow. The agentic flow must:
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extract data from an application
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run a background check via an API
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query the database for available units
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draft a lease, and
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email the tenant.
If step three fails because the AI makes a mistake in the database query and pulls a unit for the wrong city – then, steps four and five will generate a legally binding lease for a property that doesn't exist, and then send it to the client. The cost of manual remediation, lost trust, and legal liability makes anything less than near-perfect execution completely unviable.
Agentic Tools: A Path to Accuracy and Explainability
To achieve the required accuracy and explainability when agents interact with enterprise databases, developers are turning to specialized tools. QueryData is such a tool for agents, designed specifically to offer near-100% accuracy for natural language-to-query. By enabling agents to retrieve correct data, QueryData ensures that agents are well-equipped to take action.
The Key Ingredient: Comprehensive Database Context
A Large Language Model (LLM) inherently knows many dialects of SQL, but it doesn't know your business logic and your database. Agentic tools use context to bridge that gap. Context is essentially the code which a tool like QueryData uses to guide the LLM towards correct answers. Crucially for achieving near-100% accuracy and explainability, the QueryData works with a comprehensive database context, organized into three main pillars: Schema Ontology, Query Blueprints and Value Searches.
1. Schema Ontology
Schema ontology is about understanding your database structure and semantics. This includes natural language descriptions of tables and columns. The QueryData LLM has a greater chance to translate the natural language question into the correct query using these instructions. You can think of schema ontology as a set of “cues” or “hints” – meant to steer the LLM into picking the right tables and columns and synthesizing them correctly into a database query. A couple of examples:
Here is what a database-level description could look like for a search engine of real estate listings:
“Listings, real estate agents and information about communities where listings are located – schools, amenities and hazards: fire, flood and noise”
The table description for property could look like this:
“Current real estate listing, including houses, townhomes, condos and land”
An example of column description that explains that the proximity_miles means
“property distance from the district’s school in miles”
For ease of use, you can autogenerate rich descriptions, which will typically include sample values of the column.
2. Query Blueprints
If ontology is the vocabulary, query blueprints are the way to introduce fine control of the generated SQL for important questions that must absolutely receive accurate and business-relevant answers. For example, consider the question “Riverside houses close to good schools”. The interpretation of “close” and “good” provided by Gemini is impressive- in a demo application it translated to
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WHERE city_name = 'Riverside' AND school_ranking 🔗 Google IA
https://cloud.google.com/blog/products/databases/how-to-get-your-agent-near-100-percent-accurate-data/?utm_source=dlvr.it&utm_medium=blogger
