#ai-agents
Every summary, chronological. Filter by category, tag, or source from the rail.
Scaling AI Agents: From Tribal Knowledge to Production Systems
Building reliable AI agents for enterprise requires moving beyond 'vibe coding' to a rigorous system of SOP translation, where the refining loop and feedback infrastructure are 20x more important than the agent runtime itself.
AI EngineerScaling AI Agents: Lessons from Snowflake's GTM Assistant
Successfully deploying AI agents at scale requires prioritizing quality over coverage, aggressive change management, and a willingness to rearchitect as user expectations evolve.
AI EngineerBuilding Blocks of Go-to-Market Orchestration
Go-to-market orchestration is about moving from manual, siloed campaigns to describing intent and having agents execute across channels. The key is building a unified data substrate and solving narrow, vertical use cases before scaling horizontally.
Closing the Reinforcement Gap for Enterprise AI Agents
Arga Labs is building digital twins of enterprise software like Salesforce and Outlook to provide repeatable, sandbox environments for training AI agents, overcoming the lack of testable infrastructure in business applications.
Runable's $21M Bet on AI Agents for Business Growth
Runable is pivoting from AI-assisted software creation to end-to-end business growth, using AI agents to manage marketing, SEO, and customer acquisition for non-technical small business owners.
Keenable: Building Web Search Infrastructure for AI Agents
Keenable is a new startup building a specialized search index of over 100 billion documents designed specifically for AI agents, aiming to provide a more cost-efficient and performant alternative to traditional search APIs.
AI Agents: Why the Harness Matters More Than the Model
AI system performance is driven by the 'agentic harness'—the tools, memory, and loops surrounding the model—rather than just the model itself. Distinguishing between the 'brain' (model) and the 'jar' (harness) is essential for building effective AI agents.
OpenAI's Shift to Agentic Workflows for Non-Engineers
OpenAI is expanding beyond coding tools with 'ChatGPT Work,' an agentic platform designed to automate complex, multi-step tasks across common business software, aiming to move AI from simple Q&A to autonomous project execution.
Human Judgment in the Age of AI Software Factories
As AI agents scale development, human judgment shifts from writing code to defining intent, system design, and verification strategy. A 'software factory'—a repeatable, event-driven loop—is the framework for managing this shift, provided you balance verification budgets with human oversight.
Bridging SQL and Vector Data with Agentic Workflows
Digital librarian AI agents solve the 'what vs. why' data gap by orchestrating queries across structured SQL databases and unstructured vector databases to provide grounded, context-aware answers.
IBM TechnologySolving Alignment Bottlenecks in Chip Design with AI
In high-stakes industries like chip design, alignment is a quadratic cost that outweighs individual skill. A shared nervous system—using a living graph of intent and role-specific agents—can reduce communication overhead and prevent costly errors.
AI EngineerFinOps for AI Agents: Implementing Run-Level Token Governance
Token Ops introduces a control plane that manages AI agent costs at the run-level using 'steering'—injecting instructions to reduce token consumption—rather than just hard-capping or killing processes.
Building Trust in Multi-Agent AI Science via Auditable Records
To enable reliable collaboration among AI scientist agents, communities must implement auditable, immutable record-keeping systems that ensure transparency, reproducibility, and accountability in agent-led research.
The AI Harness: Why Scaffolding Outperforms Model Intelligence
Nvidia research demonstrates that the 'harness'—the system of memory, tools, and supervisory logic surrounding an LLM—is more critical for long-horizon agent performance than the underlying model itself.
Scaling Agentic SDLC at Uber
Uber has shifted 70% of pull requests to AI agents by building a standardized infrastructure layer that manages model security, context retrieval, and automated validation, effectively moving the engineering bottleneck from 'how to build' to 'what to build'.
Collusion Risks in AI Agents and the Case for Market Certification
As AI reasoning agents increasingly participate in market decisions, their potential to engage in tacit collusion necessitates new certification frameworks to ensure economic stability and fair competition.
AI Agents vs. Business Rules: A Hybrid Decision Framework
AI agents do not replace business rules; they complement them. Use deterministic rules for predictable, high-volume logic and probabilistic AI agents for unstructured data, nuanced judgment, and complex tool-calling workflows.
IBM TechnologyBinance Agent OS: Enabling Autonomous Crypto Trading
Binance has launched Agent OS, a platform allowing AI agents to execute trades and manage financial workflows, shifting the burden of risk management and security entirely onto the user.
Runtime Governance for Agentic AI: Action-Boundary Control
The article proposes a framework for securing autonomous agents by enforcing strict action boundaries, cryptographic provenance, and a fail-closed execution model to prevent unauthorized or dangerous operations.
Building Bidirectional Multimodal AI Agents
Moving from turn-based chatbots to 'omni-apps' requires a continuous loop of perception, reasoning, and expression that handles real-time voice, vision, and browser interaction.
Google Cloud TechArchitecting Enterprise AI Agents for Regulated Environments
Enterprise AI agents fail in production because compliance requirements are bolted on as an afterthought. Instead, build systems using immutable event logs, segregated object storage, and human-agent parity to make auditability and evaluation inherent to the architecture.
Using X12 as an Agentic Harness for Healthcare Claims
To build reliable healthcare AI agents, treat the X12 standard as a structural harness rather than just a file format. This grounds agentic reasoning in industry-standard transactions, providing a reliable execution layer that balances flexibility with necessary constraints.
Building AI Agents with Gemini Enterprise & Google Workspace
Learn how to integrate Gemini Enterprise agents with Google Workspace data and actions using connectors, MCPs, and no-code/pro-code development frameworks to automate enterprise workflows.
Google Cloud TechNavigating the AI Security Trilemma: Smart, Fast, or Secure
Enterprises face a 'trilemma' where AI systems can only optimize for two of three pillars: intelligence, speed, or security. Achieving all three requires architectural interventions like security proxies to offload guardrails from the model.
Evaluating Internal Action Maps Without Global Affine Closure
This paper introduces a calibrated testing framework for internal action maps in AI agents, demonstrating that state signals can be effectively processed without requiring global affine closure.
Democratizing Startup Funding with AI Agents
Happly.ai uses AI vectorization and Gemini to help founders secure non-dilutive funding—grants, tax credits, and procurements—leveling the playing field for underrepresented entrepreneurs.
Google Cloud TechSecuring AI Agents with Claw Patrol
To secure AI agents with production access, treat them as untrusted software and intercept their actions at the wire protocol level using a proxy, rather than relying on internal model alignment or HTTP-layer guardrails.
Applying RAD Methodology to AI-Driven Development
Rapid Application Development (RAD) provides a proven framework for AI coding: plan lightly, prototype iteratively, and use spec-driven development to bridge the gap between AI-generated prototypes and production-ready software.
5 Patterns for Connecting AI Agents to Tools
Connecting AI agents to tools requires balancing usability with security. The progression moves from simple direct API connections to secure, vault-based architectures that use short-lived credentials and token exchange to ensure full observability and identity verification.
IBM TechnologyQuerying and Acting on Cloud Data with Data Agent Kit
The Data Agent Kit provides a unified framework of MCP servers, agent skills, and IDE integrations that allow AI agents to securely query, analyze, and modify data across BigQuery, Cloud SQL, and Cloud Storage.
Google Cloud TechShowing 30 of 169