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Why Harness AI Uses a Knowledge Graph, Not Raw APIs
Harness AI agents now rely on a schema‑driven Knowledge Graph instead of raw Model Context Protocol (MCP) API calls. By modeling entities, relationships, and field metadata, the platform can answer multi‑module queries with Harness Query Language (HQL) in just two to three LLM calls. This approach slashes token consumption by 15‑25× and guarantees deterministic results. The architecture is organized into a four‑tier data‑ownership model that pushes as much data as possible into the Knowledge Graph for cost‑effective, reliable AI orchestration.
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The Enterprise AI Blueprint
Enterprises often mistake impressive AI demos for production‑ready solutions, but the real challenge lies in scaling models across heterogeneous environments. Shubham Jindal explains that the gap is not model quality but the surrounding infrastructure—context engineering, rigorous evaluation, persistent memory, and...
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An Introduction to Disaster Recovery Testing in 2026
Disaster recovery (DR) testing has become a continuous discipline in 2026 as cyber‑attacks, cloud outages, and supply‑chain disruptions threaten uptime. Modern platforms combine automation, AI, and chaos engineering to turn once‑a‑year fire drills into frequent, low‑risk validations. The article outlines...
Your Repo Is a Knowledge Graph. You Just Don't Query It Yet
The article argues that traditional source‑code management (SCM) must evolve into Source Context Management to support AI agents throughout the software development lifecycle. It highlights how agents currently scrape files, leading to context‑window bloat and semantic blindness, and proposes a...
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Load Testing: An Essential Guide for 2026
Load testing has become a non‑negotiable practice for modern digital businesses, simulating real‑world traffic to verify response times, throughput, and error rates under expected and peak loads. The guide outlines a step‑by‑step methodology, from defining objectives to integrating tests into...
Branch-Scoped Sequence IDs in Harness CI
Harness CI introduced branch‑scoped sequence IDs, letting each Git branch maintain its own incremental build counter via the expression. This replaces the traditional global counter that creates gaps and confusion across main, develop, and feature branches. The feature includes...
Argo CD Install: Helm-Based Setup for Enterprise DevOps Team
The article outlines an enterprise‑grade installation of Argo CD using Helm, emphasizing repeatable, version‑pinned deployments. It details prerequisites such as a Kubernetes cluster, ingress with TLS, and SSO integration, then walks through Helm chart setup, namespace isolation, and configuration of secure...
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Designing MCP for the Age of AI Agents
Harness released MCP server v2, slashing the tool count from over 130 to just 11 while still covering 125+ resource types across its DevOps platform. The redesign replaces a one‑tool‑per‑API model with a registry‑based dispatch system, cutting context‑window consumption from roughly...
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The Agent-Native Repo: Why AGENTS.MD Is the New Standard
The article introduces AGENTS.md as a standardized, tool‑agnostic instruction file that makes code repositories agent‑native. It argues that AI coding agents fail mainly due to ambiguous repository context, not reasoning limits, and that a dedicated AGENTS.md layer solves fragmentation across...
Harness Evolves Chaos Engineering to Resilience Testing
Harness announced the evolution of its Chaos Engineering module into a broader Resilience Testing platform, adding native load and disaster‑recovery testing capabilities. The new suite builds on the open‑source LitmusChaos project and Harness’s 2022 acquisition of Chaos Native, integrating AI‑driven...
AI & Data Security: Insights From IBM’s Chief Architect
IBM’s Chief Architect Devan Shah outlines how the company’s OnePipeline platform now supports over 450 developers by shifting from Travis CI to Tekton and Argo CD, trading longer build times for automated security scans. He details the internal AI coding assistant...
Move Harness Projects Between Orgs Without Starting Over
Harness has introduced Project Movement, a feature that lets users transfer entire projects between organizations with a few clicks. The migration preserves pipelines, execution history, services, environments, and most configuration artifacts, eliminating the need to rebuild setups after org restructures....
Open Source Liquibase MongoDB Native Executor by Harness
Leveraging its Database DevOps platform, Harness has released an open‑source native MongoDB executor for Liquibase Community Edition. The executor allows teams to run MongoDB scripts, generate changelogs, and embed migrations into CI/CD pipelines without purchasing commercial extensions. Accompanying the tool...
Top CI Metrics Platform Engineering Leaders Should Track
Platform engineering leaders are urged to adopt a focused set of CI metrics—build duration (p50/p95), queue time, success rate, cost per build, flaky‑test rate, and artifact integrity—to turn raw pipeline data into actionable insight. By automating collection and visualizing these...
From Messy to Modular: Rebuilding Filters in React
Harness rebuilt its execution‑listing filters with a modular React component built on the Context API. The new system replaces a hidden side‑panel legacy UI that lost state on refresh with a discoverable, type‑safe framework. It centralizes filter state in a...
