Tag: Enterprise AI
OpenAI’s Codex Context Cut Puts Enterprise AI Coding Workflows on Notice
OpenAI quietly trimmed the default input context window for GPT-5.6 inside its Codex CLI, dropping it from 372,000 tokens to 272,000 tokens. That's a 27% cut, and developers noticed fast. The change ...
AI-Generated Code Is Cheap But the Context Infrastructure Behind It Is Not
Saqib Jan | | AI coding, context engineering, Enterprise AI, RAG, retrieval-augmented generation, vector databasesThe cost curve for generating code with AI has moved in one direction, and it has moved fast. What used to require a senior engineer's full attention for an afternoon can now ...
Anaconda Acquires Kilo Code to Unify AI Development from First Prompt to Production
Jon Swartz | | acquisition, Agentic Workflows, AI agents, AI models, Anaconda, Enterprise AI, gitlab, JetBrains, Kilo Code, Metaflow, Outerbounds, VS CodeAnaconda Inc. has acquired Kilo Code, an open-source, model-agnostic platform that embeds artificial intelligence (AI) agents directly into developer workflows. The acquisition integrates Kilo’s rapid-growth community of over three million developers into ...
Anaconda Doesn’t Want to Be Just the Python Company Anymore
The question is no longer whether Anaconda wants to be more than the Python company. It is how much of the AI development stack DeSanto ultimately intends to own ...
Reliability Comes From the System, Not the Agent
Andrew Filev | | AI agent reliability, AI agents, AI Governance, Enterprise AI, Operational Excellence, workflow designOne of the most common questions executives ask right now sounds straightforward: is the agent reliable enough yet? It feels like the right place to start, but the framing quietly points people ...
Anthropic Adds Enterprise Gateway to Simplify Claude Code Access on AWS and Google Cloud
Tom Smith | | AI coding tools, AI Governance, cost control, Enterprise AI, Google Workspace, identity management, OTLP, policy enforcement, self-hosted gateway, single sign-on, spend caps, spend management, SSO, telemetry, usage trackingAnthropic's new Claude apps gateway brings SSO, central policy, and spend caps to Claude Code on Amazon Bedrock and Google Cloud ...
The Death of the Four Golden Signals: Designing Telemetry for Non-Deterministic Infrastructure
Jyostna Seelam | | AI architecture, AI compliance, AI Governance, AI guardrails, AI infrastructure, AI monitoring, AI observability, AI operations, AI operations strategy., AI performance metrics, AI platform monitoring, AI reliability, AI resilience, AI safety, AI SLIs, AI SLOs, AI system reliability, AI telemetry, AI trust, Enterprise AI, generative AI monitoring, guardrail intervention rate, hallucination detection, LLM monitoring, LLM observability, modern SRE, non-deterministic systems, Observability Engineering, OpenTelemetry, platform engineering, production AI, RAG observability, RAG systems, semantic drift, site reliability engineering, SRE, time to first token, token throughput, TTFT, vector database monitoringIn complex software systems, our traditional definition of operational health has always been comfortably binary. For over a decade, site reliability engineering (SRE) teams have relied on the industry-standard ‘Four Golden Signals’ ...
Why Enterprise AI Infrastructure Is Becoming a DevOps Problem
Most enterprise AI projects start with retrieval. You connect Jira, Confluence, SharePoint, and Slack. Maybe a few internal databases nobody has touched in five years. You tune embeddings, optimize chunking, wire up ...
The Automation Layer Wants to Own Enterprise AI
Alan Shimel | | AI agents, Automation Layer, Enterprise AI, Infrastructure Governance, Operational Trust, platform engineering, Probabilistic Systems, runtime securityOrganizations want AI systems capable of prioritizing alerts, routing workflows, coordinating across applications, initiating remediation steps, summarizing operational data and adapting dynamically based on changing context. The system is no longer following ...
Reimagining AI-Assisted Coding for Team Scale in Enterprises
AI coding tools have advanced rapidly, but most are still designed around the individual developer. Their effectiveness depends heavily on a user’s ability to write understandable prompts, preserve architectural coherence across sessions, ...
Why Techstrong is Heading to Prague for SUSECON: Sovereignty, Open Infrastructure and the Future of AI
Alan Shimel | | cloud-native, digital sovereignty, edge computing, Enterprise AI, kubernetes, linux, Open Infrastructure, Prague, strategic resilience, SUSECON 2026As enterprises worldwide rethink control of the stack in the age of AI, SUSECON lands at the intersection of sovereignty, open infrastructure and the next generation of enterprise platforms ...
Why Your AI Agent Strategy is Failing (and How to Fix It): The Microservices Playbook for AI Agents
Lokhesh Ujhoodha | | agentic AI, AI agent transparency, AI agent trust, AI agents, AI architecture, AI Governance, AI infrastructure, AI microservices, AI observability, AI ROI, autonomous agents, CIO AI strategy, Enterprise AI, enterprise AI strategy, LLM adoption, microservices architecture, monolithic AI agentsDespite billions in AI investment and countless vendor promises, most enterprises are still treating AI agents like glorified copilots rather than autonomous systems. After working with numerous enterprise customers implementing AI agents across various ...
