Platform Pulse: Agentic Architecture, Observability, and Telemetry Routing

🤖 Agentic AI, LLMs & Confidential Compute

The ecosystem is maturing beyond basic chat interfaces, focusing heavily on programmatic agent execution, local hardware orchestration, and cross-platform communication protocols.

1. Building Multi-Agent Systems with Google ADK: A practical introduction exploring how to construct code-first AI frameworks using Google’s Agent Development Kit.
2. Cross-Language Multi-Agent Teams via A2A: Developers can now orchestrate cross-language AI teams by connecting ADK environments through the Agent-to-Agent (A2A) protocol.
3. Managed MCP Servers and ADK: Google Cloud has standardised how AI agents connect to services like BigQuery and Cloud Run by providing secure, pre-built integrations via remote Model Context Protocol (MCP) servers.
4. Mastering Gemini Computer Use: Moving past fragile CSS selectors, Gemini Computer Use empowers AI agents to visually interpret screenshots and perform native actions like clicking and typing across web and legacy desktop software.
5. AgenticOps Demo: A demonstration outlining how to manage an entire Google Cloud architecture using AI agents powered by Gemini, ADK, and MCP servers.
6. Measuring What Matters with Jules: Google Labs researchers are building a new evaluation method to test the "insight policy" of proactive coding agents, using historical bug fixes to measure how well agents navigate higher-level goals.
7. Verifiable, Private AI with Apple: Google Cloud has expanded its Confidential Computing frontiers by collaborating with Apple on its recently announced Private Cloud Compute (PCC) systems.
8. Accelerating Telecom Innovation with Gemma: AT&T and GSMA are leveraging open, domain-specific Gemma models to achieve dramatic network automation and streamline complex agentic workflows.
9. From ADK to Gemini Enterprise: A deep-dive architectural guide explaining how to scale autonomous enterprise workflows up to production-grade using the Vertex AI Agent Builder ecosystem.

📊 Analytics, Databases & BigQuery Optimisation

Data platforms are focusing heavily on performance tuning, incremental loading efficiencies, and bridging the gap between transactional systems and analytical warehouses.

1. Managed Python UDFs in BigQuery: Now generally available, Managed Python User-Defined Functions allow data engineers to define and execute Python scalar functions natively inside BigQuery SQL queries.
2. Unlocking Key Driver Analysis: BigQuery has launched AI.KEY_DRIVERS in Public Preview, allowing teams to identify specific data segments that cause statistically significant changes to summable metrics.
3. The Hidden Powerhouse: BigQuery Storage Write API: This highly optimised, gRPC-based engine leverages Protocol Buffers and bidirectional streaming to drastically improve data ingestion performance over legacy methods.
4. Fine-Grained DML for Transactional Use Cases: BigQuery's Fine-grained DML feature separates logical and physical data changes, enabling faster and cheaper real-time updates and deletions.
5. You Probably Don’t Need a Vector Database: A proof-of-concept demonstrating how to build an end-to-end RAG architecture in pure SQL directly within BigQuery, eliminating data movement and infrastructure overhead.
6. Dataform Incremental Load Optimisations: Practical pipeline techniques designed to dramatically cut execution time and computing costs for production Dataform environments.
7. Environment Promotion and Isolation in Dataform: Overcoming basic CI/CD limits by using Git Tag Promotion or an External State Store to enforce programmatic compilation and version pinning.
8. Cutting Your BigQuery Bill in Half: An essential FinOps review comparing on-demand versus slots billing, alongside deep dives into partitioning, clustering, and pre-flight preview checks.
9. Architectural Trade-offs in Data Foundations for Agentic AI: A strategic guide explaining when to route agents to low-latency operational databases (AlloyDB) versus high-throughput analytical warehouses (BigQuery).

🔐 Security, Identity & Threat Intelligence

Security parameters are tightening around access layers, with a strong focus on tracking AI threats, combating zero-day exploits, and transitioning to passwordless authentication.

1. VPC Service Controls for Agentic AI: New capabilities in VPC Service Controls allow security teams to establish destination-based, network-level perimeter guardrails specifically designed for agentic workloads.
2. Zero-Day Exploitation in Cisco Catalyst SD-WAN Manager: Threat intelligence reports confirm that CVE-2026-20245 was actively exploited to escalate privileges from an administrative account to full root-level access.
3. STOCKSTAY Another Day: Security researchers have analysed STOCKSTAY, an intelligence-gathering backdoor continually deployed by the Russia-linked threat actor Turla.
4. Ingesting Cisco Switch Syslogs into Google SecOps: A hands-on guide detailing how to bridge legacy network hardware with cloud security platforms by routing syslogs through a headless Raspberry Pi 5.
5. Passwordless Between Google Cloud and MongoDB Atlas: Utilising Workload Identity Federation to authenticate applications via short-lived cryptographic tokens, completely removing the need to store static usernames or passwords.

⚡ Cloud-Native Infrastructure & Serverless Deployments

Operational teams are upgrading application pipelines by replacing legacy queue managers, bridging OLAP/OLTP gaps with containers, and expanding telemetry queries.

1. Log Analytics is now Observability Analytics: Google has officially rebranded Log Analytics; the new Observability Analytics platform allows engineers to query logs and traces using SQL and manage buckets via API.
2. SQL Alerting in Cloud Monitoring Observability Analytics: Engineers can now directly formulate alerts based on the outputs of analytical SQL queries targeting system logs and traces.
3. From pg-boss to Cloud Tasks: An architectural migration story outlining how transitioning to Google Cloud Tasks eliminated queue bursts and database connection timeouts on serverless functions.
4. Accelerating TPU Model Loading on GKE: By integrating the Run:ai Model Streamer with Cloud Storage, teams can bypass local disks and eliminate double-buffering, saving RAM and drastically cutting vLLM cold starts.
5. Bridging the OLAP/OLTP Divide with Python and Kestra: Replacing standard reverse ETL tools with a container-first, stateless bridge that uses row_hash in BigQuery to rapidly synchronise updates into Firestore.
6. R Shiny and Google Cloud Run: Packaging R analysis applications into Docker containers to deploy them securely and scalably as public-facing serverless dashboards on Cloud Run.
7. Zero-flicker Firestore SSR with React: Utilising Firebase JS SDK APIs to build server-rendered Next.js applications that hand off to real-time client syncs without visual layout flickering or double reads.
8. The Starter Tier for Google AI Studio Explained: Launch AI prototypes quickly without a billing account by deploying web applications backed by Cloud Run and Firestore via the new Starter Tier.
9. Enhanced Data Resilience with Cross-Region Backups: Backup and DR Service now allows architectures to store their recovery data in regions completely distinct from their primary operational workloads.