AI agent Archives

Tech Optimizer

August 13, 2026

Electric joins Databricks to bring WASM Postgres to AI agent sandboxes

Databricks has acquired Electric, the team behind the open-source PGlite and real-time data synchronization technology, to enhance its Postgres capabilities. This acquisition aims to address challenges faced by AI developers in managing real-time, distributed state while minimizing network latency. Electric's technology will integrate with Databricks’ Lakebase infrastructure, allowing for production-scale Postgres capabilities in local execution environments. This development provides a unified Postgres standard for creating full-stack agentic applications across cloud and edge environments. Key features include local database access through PGlite within agent sandboxes and continuous real-time synchronization for multi-agent teams, maintaining central governance within Lakebase.

AppWizard

August 12, 2026

Google’s Android chief lays out his vision for how AI will change our smartphones

Google introduced new features in its Pixel 11 Pro smartphone, including a light that indicates when its Gemini AI is processing commands and a forthcoming Android feature named Halo that provides real-time updates on AI task progress. The company aims to reduce the need for users to micromanage their devices by allowing AI to handle routine tasks, such as scheduling appointments. Google anticipates consumer needs three years ahead in its Android development, focusing on how AI can enhance user experience. Other companies, like Samsung and Apple, are also investing in AI integration, with Apple planning to revamp Siri for better functionality. Despite advancements, there is growing consumer skepticism about AI, with 79% of Americans expressing distrust in businesses to use AI responsibly.

Tech Optimizer

August 12, 2026

Databricks acquires Electric to give every AI agent its own Postgres database

Databricks has acquired Electric, a startup known for its WASM-based Postgres project, PGlite, and the Electric sync engine. The Electric team will integrate with Neon, a serverless Postgres company acquired by Databricks for approximately billion last year. PGlite is a full Postgres database implemented in WebAssembly, capable of running in various environments and supporting dynamic extension loading, including pgvector. PGlite's weekly downloads increased from 1 million to 13 million over the past year. The Electric sync engine allows near real-time synchronization of a central Postgres database across platforms, similar to collaborative tools like Figma and Google Docs. Electric's founders have assured that all previously open-sourced components will remain accessible, while the hosted service, Electric Cloud, will be phased out. Databricks has noted that agents are now responsible for creating four times more databases than human users on Lakebase, with the average lifespan of a database compute instance being less than 10 seconds. The architecture of Lakebase separates storage from compute, facilitating cost-effective operations. PGlite originated from an experiment by Stas Kelvich, co-founder of Neon, who compiled Postgres to WASM, which Electric later developed into a production project.

Tech Optimizer

August 12, 2026

Dealroom.co | Databricks acquires Electric to bring Postgres to AI agent sandboxes

Databricks has acquired Electric, the creator of PGlite, a lightweight version of Postgres that operates within applications and user devices instead of relying on a separate server. This acquisition enhances Databricks' Postgres capabilities, allowing AI agents to maintain local Postgres instances while synchronizing with a central Lakebase Postgres system. Electric's real-time synchronization engine ensures that distributed agents remain aligned with a central record. The acquisition comes as companies shift towards agentic applications that require reliable data and rapidly changing local context. PGlite has gained significant traction, with weekly downloads increasing from 1 million to 13 million between August 2025 and August 2026. Databricks views Postgres as a foundational element for AI agents, emphasizing the importance of managing state, context, and synchronization among agents in the evolving landscape of AI systems.

Tech Optimizer

August 6, 2026

Antivirus software alone does not guarantee protection, this expert warns

Hackers have stolen over 0 million USD in Bitcoin by exploiting vulnerabilities in Canadian-made hardware wallet keys. The breach involved sophisticated tactics, including the use of an AI agent that deceived targets into installing malware and could alter its identity to avoid detection. Ritesh Kotak emphasized that reliance solely on antivirus software is insufficient, highlighting the need for a comprehensive approach to cybersecurity. The incident raises concerns about the effectiveness of current protective measures and the growing threat landscape in the digital economy.

Tech Optimizer

July 30, 2026

Generating Referentially Consistent Synthetic Test Data for PostgreSQL in .NET Application Modernization

Satyajit Gokhale, Aiswaryaa Venugopalan, Ruchi Agarwal, Nits Jeganathan, Khurram Khawaja, Vijay Mandadi, and Luke Huan contributed to this article. AWS Transform offers a .NET application modernization service that allows enterprise customers to migrate from Microsoft SQL Server to Amazon Aurora PostgreSQL Compatible Edition. This migration reduces licensing costs and enhances scalability while utilizing an open-source ecosystem. Schema conversion tools can assist in translating DDL to PostgreSQL-compatible definitions, but correctness must be verified by teams to ensure performance under real-world workloads. Traditional testing methods, such as using production data or manual checks, are not scalable and raise privacy concerns. Therefore, there is a need for realistic synthetic data generated at scale that adheres to the migrated schema's constraints. AWS Transform for SQL Server modernization employs an AI agent to generate constraint-respecting synthetic data at scale during the migration workflow. This process involves loading the PostgreSQL schema into a live database, constructing a dependency graph for data generation, and distributing tasks across multiple sub-agents. The agent validates each generated INSERT against the live database, ensuring compliance with constraints and providing error diagnostics for any violations. The synthetic data generation workflow consists of four stages: schema analysis, establishing generation order, distributing tasks across sub-agents, and validation with error recovery. The agent extracts metadata from the PostgreSQL schema, determines a valid table generation sequence, parallelizes data generation while maintaining referential integrity, and generates and validates data row-by-row against the live database. Each successful insertion enriches the context for subsequent generations, while errors are handled through structured diagnostics and iterative regeneration. The process ensures referential integrity and preserves the generation state, allowing for recovery from interruptions without data loss.

AppWizard

July 28, 2026

AI taught to design efficient farms in Minecraft – mathematical modeling and constraints are used

The emergence of virtual assistants in gaming, particularly in agricultural simulation within Minecraft, enhances the gaming experience by providing players with instructions and explanations for farming tasks. A developer named Serranegra has created an AI tool that optimizes farm construction by generating an optimal arrangement of blocks based on player input regarding terrain and desired crops. For example, the AI's solution for sugarcane cultivation increases yield from 54 to 61 units while improving land utilization from 66.7% to 75.3%, representing a 13% efficiency increase. However, the tool's benefits vary by crop, with negligible gains for some, such as cacti and wheat. Serranegra has made the source code of the "optifarm" project publicly available on GitHub for collaboration.

Tech Optimizer

July 23, 2026

Simplify AI agent orchestration with Lakebase Postgres

The auditing process has traditionally been labor-intensive, requiring extensive document reviews. CLA (CliftonLarsonAllen LLP) partnered with the Databricks Forward Deployed Engineering team to develop a document processing application that reduces extraction time from hours to minutes while maintaining quality. The application is built on the Databricks platform, utilizing technologies like Lakebase Postgres, Databricks Apps, Lakeflow Jobs, MLflow, and Unity Catalog Volumes. The application addresses challenges in document parsing, including unpredictable per-task latency, rate-limit-aware throttling, workload prioritization, cost attribution per task, and real-time progress visibility. It eliminates the need for multiple specialized systems by integrating components such as a web application for user interaction, Lakebase for state management, an orchestrator for task management, and AI agents for document processing. The task queue is supported by two Postgres tables in Lakebase, tracking task status and execution attempts. Concurrency safety is ensured through a locking mechanism, and crash recovery is facilitated by lease-based locking. The orchestrator implements rate-limit-aware throttling and idempotent webhook callbacks to manage task processing effectively. An operator dashboard provides real-time metrics on task status, agent performance, and workload costs, with live updates through Postgres LISTEN/NOTIFY events. Cost attribution is managed at the application level, allowing for detailed monitoring of expenditures. Lakebase enhances the orchestration process with features like autoscaling compute, OAuth-rotated authentication, Unity Catalog integration, and support for branching and snapshots.

Tech Optimizer

July 12, 2026

What Is Serverless PostgreSQL?

Serverless PostgreSQL is a fully managed cloud database model that separates compute and storage, allowing them to scale independently and automatically based on demand. It eliminates the need for manual infrastructure provisioning and capacity planning, charging only for active usage. Unlike traditional PostgreSQL setups, which require continuous resource allocation and manual scaling, serverless PostgreSQL provisions resources on demand and can scale down to zero during idle periods. Serverless PostgreSQL integrates with serverless compute platforms, enabling analytical queries to access the same data within a unified architecture. Key differences between traditional and serverless PostgreSQL include manual versus automatic provisioning and scaling, fixed versus usage-based billing, and high versus reduced operational overhead. Lakebase architecture is an emerging model that combines transactional databases with lakehouse foundations, allowing operational and analytical workloads to coexist on a single platform. This architecture minimizes data duplication and simplifies access, enhancing data management and analysis. Serverless PostgreSQL operates on a cloud-native architecture that enhances efficiency by allowing compute and storage to scale autonomously. It features scale-to-zero behavior, where compute resources are suspended when inactive and reactivated upon new queries. Major providers include Databricks Lakebase, Amazon Aurora Serverless v2, and Neon, each offering varying capabilities and integrations. Pricing for serverless PostgreSQL typically includes charges for compute resources, storage, and data transfer, with costs fluctuating based on workload activity. Cold start latency is a performance consideration, as reactivating compute resources can introduce delays. Strategies to mitigate this include keeping resources partially active or selecting providers with minimal cold start impacts. Serverless PostgreSQL is well-suited for OLTP workloads, while lakebase architecture is better for AI development, variable workloads, and environments requiring rapid iteration. Setting up serverless PostgreSQL involves choosing a provider, creating a database instance, and configuring access settings. It can also be used alongside serverless compute platforms for analytics, further extending its capabilities.

Tech Optimizer

July 10, 2026

Google Cloud: PostgreSQL community contribution updates

Google Cloud actively participates in the PostgreSQL ecosystem by supporting community-driven events and contributing to open-source initiatives. Recent key events include: - **PGConf.dev 2026**: Featured strategic discussions on logical replication and global index architecture, with a consensus to adopt a deparsing-based approach for DDL replication. Dilip Kumar presented on global indexes. - **PGConf India 2026**: Attracted over 580 participants, featuring various sessions including keynotes and technical talks by Google Cloud contributors. - **PGDay Paris & PGDay France 2026**: Matt Cornillon was involved in organizing PGDay France, with sessions led by him and Yves Colin. - **PGDay FOSDEM 2026**: Focused on AI-assisted workflows in PostgreSQL development, with a technical talk by Matt Cornillon. - **PGConf Belgium 2026**: The session was selected as supplementary material for a database exam, indicating student engagement. - **Nordic PG Day 2026**: Google participated as a Partner-level sponsor and hosted a dedicated booth. - **Swiss PGDay 2026**: Featured a demonstration on processing vectors in PostgreSQL. - **Postgres Conference 2026 San Jose**: Google sponsored the event, with Vikas Arora discussing PostgreSQL adaptations for AI workloads. Community leadership roles included: - Dilip Kumar on the Program Committee for PGConf.dev 2026 and the Paper Selection Committee for PGConf India 2026. - Matt Cornillon on the organization committee for PGDay France, and Yves Colin on the Program Committee. Acknowledgment was given to various contributors for their dedication to PostgreSQL conferences.