Agentic workflows are essential for executing complex business logic, but they face significant challenges related to accuracy. As developers strive to implement autonomous agents, they encounter the compounding error problem, where inaccuracies can escalate through multiple dependent steps.
Consider an agent operating at 90% accuracy. In a single-step process, it would yield correct answers 90% of the time. However, in a multi-step agentic workflow, the cumulative success rate drops dramatically:
- One step: 90% success rate.
- Two steps: 81% success rate.
- Five steps: 59% success rate.
When operating at 80% accuracy, the success rate for a five-step process falls to just 33%. In many business contexts, such low accuracy is unacceptable, particularly in customer-facing applications where trust and revenue are at stake. Moreover, various industries impose legal and compliance standards that necessitate near-perfect accuracy.
Example Scenario: In a real estate agency, an AI workflow for tenant onboarding involves several critical steps:
- Extracting data from applications.
- Running background checks via APIs.
- Querying databases for available units.
- Drafting leases.
- Sending leases to tenants.
If an error occurs in the database query, subsequent steps could lead to generating a lease for a non-existent property, resulting in legal complications and loss of trust.
Tools for Enhancing Accuracy
To address these challenges, developers are turning to specialized tools like QueryData, which aims to provide near-100% accuracy for natural language queries. This tool helps agents retrieve the correct data, ensuring they can take appropriate actions.
Importance of Comprehensive Database Context
A Large Language Model (LLM) may understand SQL dialects but lacks knowledge of specific business logic and database structures. Tools like QueryData leverage context to guide LLMs effectively. This context is built on three pillars:
- Schema Ontology: This involves understanding the database structure and semantics, including natural language descriptions of tables and columns.
- Query Blueprints: These provide control over SQL generation, ensuring that critical questions receive accurate and relevant answers.
- Value Searches: These address ambiguities in natural language questions by associating data values in the database with the entities referred to in the questions.
For example, if a user queries about properties in "Westwod," value searches can identify the likely misspelling of "Westwood" and clarify its dual identity as both a city and a brokerage.
Conclusion: Building Trust Through Accuracy
As agentic workflows evolve, ensuring accuracy is paramount. By employing context engineering, businesses can reduce errors and enhance the reliability of their autonomous agents. Developers are encouraged to explore tools like QueryData to create effective context sets across various databases.
