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Inside Vector Databases: Engineering High-Dimensional Search for Modern AI Systems

Last Updated on February 19, 2026 by Editorial Team Author(s): Rizwanhoda Originally published on Towards AI. Inside Vector Databases: Engineering High-Dimensional Search for Modern AI Systems The real bottleneck in modern AI systems is not the LLM. Photo by Huzeyfe Turan on UnsplashVector databases serve as specialized infrastructure for managing high-dimensional search within modern AI systems, helping to address challenges in quickly retrieving millions of embeddings with accuracy. The article explores their architecture, benefits over traditional databases, and various applications including semantic search, recommendation systems, and multimodal data retrieval, emphasizing the need for businesses to utilize vector databases as they scale and enhance AI-driven services. Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI

DBS pilots system that lets AI agents make payments for customers

Artificial intelligence is moving closer to the point where it can act, not advise. A new pilot by DBS Bank shows how that change may soon affect everyday payments, as financial institutions begin testing systems that allow AI agents to complete purchases on behalf of customers. DBS is working with Visa to trial Visa Intelligent […] The post DBS pilots system that lets AI agents make payments for customers appeared first on AI News.

Advancing independent research on AI alignment

OpenAI commits $7.5M to The Alignment Project to fund independent AI alignment research, strengthening global efforts to address AGI safety and security risks.

Breaking the Monolith: Architecting a Process-Based Sub-Agent Ecosystem

Last Updated on February 19, 2026 by Editorial Team Author(s): Shreyash Shukla Originally published on Towards AI. Image Source: Google Gemini The “Generalist” Ceiling In the previous five articles, we architected a robust single agent. It has memory, tools, and user context. However, as we scale this agent to handle enterprise-grade complexity — such as “Deep-Dive Root Cause Analysis” or “Metric Design” — we hit a hard ceiling: The Generalist Trap. The Constraint A single agent operating on a monolithic system prompt is inherently limited. Microsoft Research notes that while single-agent architectures offer simplicity, they often falter in complex, dynamic environments where distinct, conflicting reasoning paths are required [Single-agent vs. Multi-agent architectures — Microsoft Learn]. When we try to force one agent to be an expert in everything — SQL generation, data visualization, statistical hypothesis testing, and creative metric design — we dilute its performance in anything. The context window becomes crowded with competing instructions, leading to “attention drift” where the model ignores specific constraints. The Bloat (The “One Giant Brain” Problem) From an engineering perspective, a monolithic agent is a liability. Stacking hundreds of tools and thousands of lines of instructions into a single system_prompt creates a "God Object." Debuggability: If the agent fails to write correct SQL, is it because of the SQL instruction or because the new “Creative Writing” instruction confused it? Hallucination: Research on Magentic-One (Microsoft’s Multi-Agent System) demonstrates that monolithic agents struggle with inflexible workflows, whereas encapsulating distinct skills in separate agents significantly reduces error rates [Magentic-One: A Generalist Multi-Agent System — Microsoft Research]. The Solution: The Swiss Army Knife Pattern To break this ceiling, we must transition from a “Worker” model to an “Orchestrator” model. We stop building one giant bot and start building a Team. The Shift: We decompose the monolith into a fleet of Specialist Agents (Sub-Agents). The Orchestrator: The primary agent becomes a router. Its only job is to understand intent and delegate the task to the correct expert. Validation: Industry benchmarks using frameworks like AutoGen show that multi-agent systems can increase accuracy on complex tasks by over 20% simply by allowing agents to critique and refine each other’s work [How Multi-Agent LLMs Differ from Traditional LLMs — Deepchecks]. Image Source: Google Gemini The Specialist Fleet (Process-Based Agents) To break the “Generalist Ceiling,” we adopt a Multi-Agent Architecture. Instead of a single agent attempting to be a “Jack of all trades,” we build a fleet of distinct “Experts.” This approach mirrors the “Agentic Design Patterns” advocated by AI thought leaders like Andrew Ng. His research emphasizes that decomposing a complex workflow into smaller, iterative steps handled by specialized agents (e.g., Coder, Critic) significantly outperforms zero-shot prompting. By isolating roles, we ensure each agent operates within a “perfect context” relevant only to its specific domain, reducing the noise that leads to hallucination [The Batch — Agentic Workflows]. The Roles: Defining the Experts We define three primary Specialist Agents to handle the most complex data workflows. These align with the “Mixture of Experts” pattern often discussed in advanced LLM system design: The Deep Analysis Agent (The Investigator): This agent is optimized for structured, multi-turn data slicing. It does not just “query data”; it guides the user through a hypothesis tree. It asks clarifying questions (“Do you want to slice by Region or Product?”) and suggests dimensions for investigation. The Metric Innovation Agent (The Architect): Designing a new KPI (Key Performance Indicator) requires a different cognitive mode than debugging code. This agent is instructed to act as a “Product Manager,” focusing on business logic, formula stability, and edge-case definitions before any SQL is written. The Exploration Agent (The Scout): This agent is designed for open-ended discovery. It uses “Exploratory Data Analysis” (EDA) techniques to uncover hidden patterns or anomalies in datasets without a predefined user hypothesis. The Orchestrator: The Routing Logic The core of this system is the Orchestrator (formerly the Generalist). It no longer attempts to solve every problem. Instead, it functions as a Router Chain — a concept formalized by frameworks like LangChain [LangChain Router Chains Documentation]. The Listener: The Orchestrator actively analyzes the user’s intent. If a user says, “Why did customer engagement drop last quarter?”, the Orchestrator recognizes this as a “Root Cause” problem. The Handoff: It does not try to answer. Instead, it triggers a handoff tool: transfer_to_agent(agent_name="exploration_agent"). The Result: The user is seamlessly transitioned to the expert agent, which loads its own specialized system prompt and toolset, ensuring the context window is clean and focused. Image Source: Google Gemini The Gatekeeper (Sub-Agent Access Control) In a monolithic system, access control is binary: you are either “in” or “out.” But in an Expert Ecosystem, granularity is essential. Not every user should have access to every specialist. The Risk: A “Sales Program X Agent” might have access to sensitive compensation data. The Standard: We cannot rely on the LLM to police itself (“Please don’t show this data”). According to the OWASP Top 10 for LLMs, “Model Denial of Service” and “Unauthorized Access” must be mitigated by a deterministic control layer, not by prompt engineering [OWASP Top 10 for LLM Applications]. The Mechanism: Middleware Interception To solve this, we implement a Middleware Pattern (specifically, a sub_agent_access module) that acts as a firewall between the Orchestrator and the Sub-Agents. This aligns with the NIST Zero Trust Architecture, which mandates that access to individual enterprise resources must be granted on a per-session basis [NIST SP 800-207: Zero Trust Architecture]. The Enforcement Flow We utilize a Hook System (specifically the before_tool_callback) to enforce these rules. The flow is strictly deterministic: Intercept: When the Orchestrator decides to call transfer_to_agent('Exploration_Agent'), the middleware halts the execution before the tool runs. Verify (RBAC Check): The system retrieves the user’s identity (User ID) and cross-references it against the specific Access Control List (ACL) for the target agent (e.g., Exploration_Agent_Allowed_Users). Enforce: If Authorized: The callback returns None, allowing the tool to proceed and the sub-agent to load. If Unauthorized: The callback raises a PermissionError. The tool call is rejected, […]

Engineering the Semantic Layer: Why LLMs Need “Data Shape,” Not Just “Data Schema

Last Updated on February 19, 2026 by Editorial Team Author(s): Shreyash Shukla Originally published on Towards AI. Image Source: Google Gemini The “Context Window” Economy In the world of Large Language Models (LLMs), attention is a finite currency. While context windows are expanding, the “Lost in the Middle” phenomenon remains a persistent architectural challenge. Research from Stanford University demonstrates that as the amount of retrieved context grows, the model’s ability to accurately extract specific constraints significantly degrades [Lost in the Middle: How Language Models Use Long Contexts]. This creates a critical problem for enterprise analytics. The standard industry approach — Retrieval-Augmented Generation (RAG) — often involves stuffing the prompt with hundreds of CREATE TABLE statements (DDL) in hopes that the LLM figures it out. This leads to "Context Rot." The model becomes overwhelmed by irrelevant columns and foreign keys, losing focus on the user's actual question. Furthermore, relying on raw schema introduces the “Raw Schema Fallacy.” A DDL statement describes structure, not content. It tells the agent that a column named status exists, but not what values live inside it. Does it contain "Active/Inactive"? "Open/Closed"? "1/0"? Without this knowledge, the agent is forced to hallucinate filters, producing SQL that is syntactically perfect but semantically dead. DataCamp notes that this lack of semantic context is a primary driver of the 20-40% failure rate in text-to-SQL applications [State of Data & AI Literacy 2024]. To solve this, we must abandon “Passive RAG” in favor of a “Just-in-Time” architecture that provides rich, targeted context only for the specific tables involved. Image Source: Google Gemini Pillar 1 — The Enterprise Semantic Graph The first pillar of accuracy is the Enterprise Semantic Graph. We cannot rely on static documentation or manual wiki pages, which go stale the moment they are written. Instead, we must treat the SQL ETL scripts themselves as the source of truth. By parsing the scripts that create the tables, we generate a structured, JSON-based map of the data universe. This moves us away from a flat list of tables to a Semantic Layer — a concept that Databricks argues is essential for AI, as it “translates raw data into business concepts” [The Importance of a Semantic Layer for AI]. This structure allows the agent to navigate Data Lineage — understanding not just that a table exists, but identifying its upstream dependencies and downstream consumers. Crucially, it provides the agent with Verified Logic. Instead of guessing how to calculate “Churn,” the agent reads the simplified_sql_logic directly from the metadata, ensuring the math is identical to the official reporting. The Artifact: The Knowledge Object Instead of just indexing schema, we index the logic. { "table_name": "revenue_daily_snapshot", "lineage": { "upstream_tables": [ { "table_name": "raw_bookings"}, { "table_name": "currency_conversion_rates" } ] }, "metrics": [ { "name": "Net_Revenue_Retention", "definition": "Revenue from existing customers excluding new sales.", "simplified_sql_logic": "SUM(renewal_revenue) + SUM(upsell_revenue) - SUM(churn_revenue)", "key_filters_and_conditions": ["is_active_contract = TRUE", "region != 'TEST'"] } ]} Image Source: Google Gemini Pillar 2 — Statistical Shape Detection The second pillar of accuracy is Statistical Shape Detection. While the Semantic Graph provides the map of the data, Shape Detection provides the terrain. To write accurate SQL, an agent needs to know the statistical signature of the data columns before it attempts to query them. Without this, LLMs fall into the “Cardinality Trap.” For example, if a user asks to “Group customers by type,” and the customer_type column actually contains unique IDs (high cardinality) instead of categories (low cardinality), the resulting GROUP BY query could crash the database cluster. This is why Gartner predicts that organizations adopting "active metadata" analysis will decrease the time to delivery of new data assets by as much as 70% [Harnessing Active Metadata for Data Management]. By proactively scanning for data shape, the agent removes the friction of trial-and-error querying. To prevent this, our architecture pre-computes a “Shape Definition” for every critical column. The agent references these metrics “Just-in-Time” to validate its logic: The Artifact: The Shape Definition Before writing a single line of SQL, the agent consults these pre-computed signals: DISTINCT VALUE COUNT: The agent checks this to decide if a column is safe for a GROUP BY clause (Low Cardinality) or should be treated as an identifier (High Cardinality). FREQUENT VALUES OCCURRENCES: This prevents "Value Hallucination." If the user asks for "United States" data, the agent checks this list to see if the actual value in the database is 'USA', 'US', or 'United States'. QUANTILES & MIN/MAX VALUE: These allow the agent to detect outliers. If a revenue figure is outside the 99th percentile, the agent can flag it as an anomaly rather than reporting it as a trend. ROW COUNT: These serve as "Health Checks." If the ROW_COUNT has dropped by 50% since yesterday, the agent knows the data pipeline is broken. This approach aligns with IDC’s findings that “mature data intelligence organizations” — those that actively manage metadata context — achieve 3x better business outcomes compared to their peers [IDC Snapshot: Data Intelligence Maturity Drives Three Times Better Business Outcomes]. Image Source: Google Gemini The Result — Deterministic SQL Generation When we combine these two pillars — the Enterprise Semantic Graph and Statistical Shape Detection — we achieve a fundamental shift in how the agent operates. We move from Probabilistic Text Generation to Deterministic SQL Assembly. In a standard LLM workflow, the model guesses the query based on patterns it learned during training. In our architecture, the agent acts more like a compiler. It does not guess; it assembles the query based on verified constraints: Selection (The Map): The Semantic Graph explicitly identifies my_company_data.revenue as the correct table for "Sales," rejecting similar-sounding but irrelevant tables. Filtering (The Terrain): The Shape Detector confirms that the region column contains 'NA', not 'North America', ensuring the WHERE clause actually returns data. Logic (The Rule): The Knowledge Object provides the exact formula for “Net Revenue,” preventing the agent from inventing its own math. This “Constraint-Based” approach mirrors the evolution of Self-Driving Cars, which rely on high-definition maps (Semantic Layer) […]

What it takes to make agentic AI work in retail

Thank you for joining us on the “Enterprise AI hub.” In this episode of the Infosys Knowledge Institute Podcast, Dylan Cosper speaks with Prasad Banala, director of software engineering at a large US-based retail organization, about operationalizing agentic AI across the software development lifecycle. Prasad explains how his team applies AI to validate requirements, generate…

AI Impact Summit 2026

A look at the partnerships and investments Google announced at the AI Impact Summit 2026.

“No technology has me dreaming bigger than AI”

CEO Sundar Pichai’s remarks at the opening ceremony of the AI Impact Summit 2026

After Orthogonality: Virtue-Ethical Agency and AI Alignment

Preface This essay argues that rational people don’t have goals, and that rational AIs shouldn’t have goals. Human actions are rational not because we direct them at some final ‘goals,’ but because we align actions to practices[1]: networks of actions, action-dispositions, action-evaluation criteria,

Introducing OpenAI for India

OpenAI for India expands AI access across the country—building local infrastructure, powering enterprises, and advancing workforce skills.

Ottonomy builds Ottumn.AI platform to orchestrate robots, drones, and smart infrastructure

Ottonomy built Ottumn.AI on NVIDIA's infrastructure to integrate delivery systems for healthcare, logistics, and smart cities. The post Ottonomy builds Ottumn.AI platform to orchestrate robots, drones, and smart infrastructure appeared first on The Robot Report.

This Defense Company Made AI Agents That Blow Things Up

Scout AI is using technology borrowed from the AI industry to power lethal weapons—and recently demonstrated its explosive potential.

Nvidia’s Deal With Meta Signals a New Era in Computing Power

The days of tech giants buying up discrete chips are over. AI companies now need GPUs, CPUs, and everything in between.

igus launches new RBTX online shopping experience for robotics developers

This shift to a self-serve model removes the requirement for a sales consultation, providing a streamlined experience, igus said. The post igus launches new RBTX online shopping experience for robotics developers appeared first on The Robot Report.

Mitsubishi Electric follows digital-first strategy for advanced air mobility

Mitsubishi Electric United States is focused on helping companies with digital-first initiatives such as AnyMile for drone cargo management. The post Mitsubishi Electric follows digital-first strategy for advanced air mobility appeared first on The Robot Report.

IBM and UC Berkeley Diagnose Why Enterprise Agents Fail Using IT-Bench and MAST

A new way to express yourself: Gemini can now create music

The Gemini app now features our most advanced music generation model Lyria 3, empowering anyone to make 30-second tracks using text or images.

Google’s AI music maker is coming to the Gemini app

Let Gemini be your, uh, Bard? | Image: Google Google has given Gemini the ability to spit out AI-generated music, courtesy of DeepMind's latest audio model. Beta access to Lyria 3 is rolling out in the Gemini app, enabling users to generate 30-second tracks based on text, images, and videos, without having to leave the chatbot window. The new music-making tool is available globally starting today in English, German, Spanish, French, Hindi, Japanese, Korean, and Portuguese, with plans to expand in the future. Access is limited to Gemini app users who are 18 years or older. Lyria 3's text-to-music capabilities allow Gemini app users to make songs by describing specific genres, moods, or … Read the full story at The Verge.

A new way to express yourself: Gemini can now create music

Lyria 3 is now available in the Gemini app. Create custom, high-quality 30-second tracks from text and images.