August 2026 Summaries

Harness describes a multi-layered approach to building trust in its SDLC Knowledge Graph, which translates natural-language software delivery questions into Harness Query Language and answers them using connected operational data. The company found that plausible answers could still rely on unavailable entities, unpopulated relationships, stale ingestion data, or incorrect rankings and counts, so it evaluates the full path from question and query through schema, relationships, data freshness, authoritative APIs, and the product interface. Initial schema checks validated 19 of 36 prompts, while later API- and product-backed validation covered 22 of 27 scenarios, illustrating that entity availability alone does not guarantee usable or correct results. Harness combines repeatable AI evaluations measuring correctness, groundedness, relevance, and actionability with direct graph validation and comparisons against APIs or product views, and it automates much of this work through a /validateKG Claude Code skill. The approach uses production data to expose edge cases such as retries, deleted resources, null values, and ingestion delays, while integrating expected behaviors and validation methods earlier in feature development. Looking ahead, Harness plans to broaden graph coverage, assess anomaly explanations and recommendations, and run continuous evaluations to detect regressions as its data model and platform evolve.

Artifact registry migrations are often complicated less by copying artifacts than by updating the many developer, CI/CD, Kubernetes, documentation, and credential configurations tied to legacy registry URLs. The proposed zero-downtime approach uses a compatibility layer behind a vanity DNS domain to accept legacy package requests, inject tenant context, translate package-specific URL formats into the destination registry’s native paths, and forward traffic without requiring immediate client-side changes. Its architecture separates simple ingress routing, an edge proxy for context and timeout handling, and a protocol-aware translation handler that supports Docker/OCI, typed package managers such as npm and PyPI, and filesystem-style repositories such as Maven. The approach emphasizes preserving package-manager protocol behavior, handling authentication differences between old and new systems, caching repository metadata where package type is implicit, testing specialized endpoints, and maintaining a rapid DNS rollback option. It also advises limiting initial package-type scope and selecting a destination registry with broad format support, policy-based retention, and supply-chain security capabilities.

Harness has introduced an Agent-Ready Code Repository and AI Code Review as components of its Software Delivery Agent, arguing that traditional source-control and review workflows cannot keep pace with high volumes of AI-generated pull requests. The repository is designed to support thousands of commits and pull requests per second, provide scoped permissions for agent identities through RBAC and OPA policies, and offer CLI tools optimized for efficient agent and developer workflows. AI Code Review applies customizable checks, risk-based diff grouping, reviewer and label suggestions, and mandatory verification gates before merging, while also using an organization’s SDLC Knowledge Graph to relate code changes to security policies, deployment context, and historical production incidents. Harness says the capabilities support GitHub repositories as well as its own SCM, can be migrated from major SCM platforms, and integrate storage, review, build, testing, security, and deployment under one policy engine; the company reports that internal use saved more than 10,000 engineering hours in one month.

Harness announced updated Cloud build pricing effective September 1, 2026, citing lower operating costs from fleet growth and hardware improvements, while introducing smaller Linux resource tiers and upgrading its macOS infrastructure to Apple M4 hardware. New 2-core and 4-core Linux AMD64 and arm64 options cost $0.005 and $0.010 per build minute, respectively, and Linux tiers with 16 or more cores receive roughly 20% reductions, while a new 64-core arm64 tier is also available. Windows rates decline by 33% across all listed sizes, and macOS pricing is described as becoming 40% cheaper alongside the infrastructure update, although the published rate table shows reductions for larger tiers. The principal exception is the 8-core medium tier for Linux, arm64, and macOS, whose price rises from $0.010 to $0.020 per minute, though paid accounts that have already bought cloud credits are excluded from the Linux increase.

Developer self-service often breaks down after a service is created because teams still must manually configure CI/CD pipelines, environments, deployment targets, approvals, and compliance checks, creating delays, inconsistent workarounds, and reduced operational visibility. Effective internal developer portals address this by acting as delivery control planes that use structured service metadata, reusable approved pipeline templates, and event-driven orchestration to automatically provision and configure deployment workflows. Portals should support governed promotion across development, staging, and production by querying existing testing, security, compliance, and approval systems, while also enabling declarative, policy-controlled provisioning of temporary and persistent environments. Real-time deployment status, actionable failure diagnostics, and remediation guidance can improve developer feedback loops and help platform teams identify recurring delivery problems. The discussion cautions against read-only portals, brittle tool-specific logic, governance bypasses, poorly managed partial failures, and underestimating integration maintenance costs. Harness IDP is presented as a platform that natively connects service catalogs with Harness Pipelines, policy enforcement, environment visibility, and auditability, while the recommended adoption approach is to begin with a frequent manual workflow, measure improvements in deployment time and toil, and expand incrementally.

Automated incident response platforms aim to reduce manual coordination and documentation during outages by automatically creating communication channels, video bridges, and Jira or ServiceNow tickets when an incident opens, while configurable runbooks can execute scripts, route actions by severity, and collect operational context. Harness AI SRE’s described Scribe Agent joins Zoom, Teams, or Google Meet bridges and monitors Slack or Teams conversations to generate a continuously updated incident timeline, identify key decisions and potential causes, and produce a draft post-incident review when the event closes. The system also correlates runbook output, deployment activity, pull requests, and ServiceNow change records to help responders investigate likely root causes without replacing engineering judgment. The author argues that this approach eliminates the need for dedicated scribes and repetitive status reporting, improves the consistency of audit and root-cause documentation, synchronizes follow-up actions with existing ticketing systems, and can automatically complete actions supported by the platform.

Internal developer platforms often struggle when service catalogs function mainly as inventories rather than helping developers decide what to build and how to build it, leading to outdated entries, inconsistent practices, and continued reliance on platform-team support. The piece argues that “golden paths”—maintained, opinionated workflows incorporating templates, infrastructure provisioning, CI/CD, observability, security checks, and deployment guardrails—can improve adoption by making recommended practices the simplest default. It advises organizations to begin with one high-frequency workflow, such as deploying a stateless API service, measure adoption and deployment outcomes, iterate from feedback, and maintain paths as versioned production products with clear ownership, testing, migration guidance, and retirement processes. Service catalogs remain useful for discovery and ownership information when integrated with golden paths that automatically register services, track originating templates, and identify drift. Harness IDP is presented as a platform supporting these practices through software templates, catalog integration, governance controls, versioning, rollback, and workflows for service creation and deployment.

Harness has introduced Resilience Testing (RT) Agents, which passively analyze Harness Continuous Delivery pipelines, Kubernetes configurations, deployment manifests, and pipeline history to identify potential resilience risks before production without injecting faults or modifying services. The agents recommend appropriate chaos experiments or load tests, and, when users choose to proceed, can generate, run, and interpret those tests within existing workflows. New capabilities include streamlined Kubernetes application onboarding, dashboards that track resilience posture across services and pipelines, and a Resilience Score for comparing load-test performance over time beyond throughput and latency. Load-test scripting now supports Java, JavaScript, and Python, reducing the need for teams to adopt a separate language. Harness also offers a free hosted resilience testing plan with its fault library, centralized control plane, pipeline integration, role-based access control, and hosted logging.

Internal Developer Portal success should be measured through aggregate, outcome-based metrics rather than individual developer surveillance, which can distort behavior, undermine psychological safety, and reduce adoption. Useful privacy-respecting indicators include deployment frequency, mean time to recovery, service-standardization rates, template adoption, self-service workflow completion and error rates, time to first deployment, and documentation freshness within service catalogs. These measures help platform teams assess whether portals reduce cognitive load, improve incident response, streamline onboarding, and encourage voluntary use while avoiding performance rankings based on commits, API calls, or workflow speed. The discussion presents Harness IDP as a tool for collecting organization-level analytics on catalog adoption, golden-path templates, workflow bottlenecks, and service health, arguing that effective platform measurement should guide infrastructure improvements and demonstrate ROI without exposing individual activity.

Continuous delivery often falters not because CI/CD tools lack capability, but because developers face fragmented workflows, unclear ownership, opaque pipeline requirements, and reliance on tribal knowledge. The article argues that an internal developer portal can reduce this cognitive overhead by using a service catalog as the central source of metadata for service ownership, dependencies, deployment requirements, and governance policies. Through a unified portal, developers can access standardized self-service deployment workflows while underlying CI/CD systems continue to execute builds and releases. The approach connects pre-deployment validation, environment promotion, health monitoring, rollback actions, audit trails, and incident response into a more consistent experience, while allowing platform teams to define reusable “golden paths” for testing, security scanning, progressive delivery, and policy enforcement. Harness presents its Internal Developer Portal as an implementation of this model, integrating its service catalog with Harness Continuous Delivery pipelines, infrastructure dependencies, feature flags, and operational visibility to standardize deployments and reduce delivery friction at scale.

Harness argues that modern vulnerability management must accelerate because attackers can exploit newly disclosed flaws within hours while organizations often take weeks to remediate and deploy fixes. Citing industry reports and Project Glasswing testing, it highlights both the increased discovery capability of LLM-based security tools and their challenges, including false positives, inconsistent results, latency, and cost. The company’s launch combines deterministic AI-assisted static application security testing, orchestration for LLM scanners, function-level reachability analysis, and agents designed to prioritize exploitable findings, generate and validate fixes, and prepare human-reviewed pull requests within governed CI/CD pipelines. Its Zero-Day Agent is intended to identify affected artifacts and pipelines immediately after disclosures, while virtual patching through a web application and API protection service can shield production during permanent remediation. The overall approach positions integrated scanning, triage, remediation, deployment, and temporary protection as a way to reduce response times and maintain oversight through existing policy, approval, and audit controls.

Harness built an MCP Gateway to let its AI Chat interact with external tools such as GitHub, Jira, and Confluence from within a single conversation, enabling users to make pipeline-related changes, open pull requests, and update tickets without leaving the platform. The gateway presents connected third-party applications as a unified tool provider while relying on existing Harness Connectors, RBAC, Secret Manager, and AI security controls rather than introducing separate credential or permission systems. Administrators can add supported applications without code changes or redeployments, while users can access only the tools and capabilities permitted by their roles. To support large-scale distributed operations, the system automatically restores broken or expired connections and stores conversation state centrally so sessions can continue across pod restarts or infrastructure changes. Additional governance includes per-tool approval settings, fail-closed request scoping, and runtime inspection of tool inputs and outputs for prompt injection and misuse.

Harness has launched the Harness Community at community.harness.io as an open forum for users, customers, engineers, and open-source contributors to share practical knowledge about operating the Harness software delivery platform. Covering areas including CI, CD and GitOps, feature flags, infrastructure-as-code management, chaos engineering, cloud cost management, internal developer portals, and AI DevOps agents, the community is intended to complement rather than replace formal support by enabling detailed peer discussions and real-world implementation advice. Harness employees and experienced customers will participate alongside AI agents trained on Harness documentation, with the company emphasizing that human expertise remains primary and AI serves as transparent backup assistance. The forum also provides dedicated spaces for related open-source projects such as Drone, OpenTofu, Harness Open Source, harness-cli, and LitmusChaos, and invites members to ask questions, share patterns and experiments, contribute to projects, or learn from others.

A DevOps automation platform is presented as an integrated, workflow-aware system that connects software builds, testing, deployments, infrastructure provisioning, security scanning, incident response, and rollbacks with shared context, governance, permissions, and audit trails, unlike individual tools that automate isolated tasks. The text argues that fragmented toolchains create costly handoffs and manual work, particularly as AI-assisted coding increases the volume and potential risk of software changes. AI-native platforms are described as extending scripted automation through capabilities such as selective testing, failure diagnosis, anomaly detection, continuous verification, and automated remediation. Key evaluation criteria include lifecycle coverage, actionable AI capabilities, reusable pipeline authoring, multi-cloud support, and built-in policy and audit controls. Harness is positioned as an example of this approach, combining CI, CD, GitOps, infrastructure management, security testing, AI SRE, and cloud cost management, while customer examples from Citi and Meltwater are cited to illustrate faster releases and higher pipeline throughput.

A software delivery platform is presented as an integrated system for managing the full lifecycle after code is written, combining continuous integration, delivery, security, governance, release verification, rollback, and feedback loops rather than limiting automation to build, test, and deployment as conventional CI/CD tools often do. Its proposed core capabilities include pipeline automation, progressive delivery methods such as canary and blue-green releases, embedded security scanning, policy-as-code, audit trails, role-based access control, automated rollback, test intelligence, and AI-assisted failure analysis. Organizations are advised to assess platforms by their lifecycle coverage, compatibility with existing source control, cloud, registry, and observability systems, governance model, scalability, AI functions, adoption support, and pricing rather than feature counts alone. Jenkins and GitHub Actions are characterized primarily as CI/CD tools requiring additional custom tooling for broader governance and release management, while GitLab is described as closer to an all-in-one platform but still subject to evaluation for delivery control and verification depth. The account uses Harness as an example of an AI-native platform and cites customer claims of reduced Jenkins administration costs, faster application onboarding, and increased release frequency, while noting that machine-learning verification requires baseline data and that rollback for stateful services and database migrations requires careful configuration.

DevOps is presented as a culture and set of collaborative practices, including CI/CD, infrastructure as code, monitoring, shared operational ownership, and DORA metrics, intended to improve the speed and reliability of software delivery. Platform engineering complements these practices by building internal developer platforms that provide self-service workflows, software catalogs, golden paths, automated scorecards, and governance controls, reducing duplicated work, ticket queues, onboarding delays, and developer context switching as organizations grow. The text argues that a dedicated platform investment becomes useful when DevOps processes no longer scale across many teams, while warning that platforms can fail if they lack developer input, are treated as finished projects, or provide self-service without adequate governance. It also contends that AI-assisted coding increases pressure on downstream testing, security, and release processes, making policy-as-code, GitOps, auditability, and automated remediation more important. Harness positions its software delivery platform as a unified solution for DevOps automation and platform-engineering self-service, citing United Airlines and PlayQ as examples of deployment and infrastructure-management improvements.

As infrastructure codebases and teams expand, Terraform workflows can encounter slower plans, large and contended state files, stale locks, module duplication, version drift, remote-backend latency, and insufficient collaboration controls, making changes increasingly risky and difficult to coordinate. Although splitting state or changing engines, including moving to OpenTofu, may address some concerns, the discussion argues that these steps do not by themselves solve broader workflow, governance, and organizational challenges. It presents infrastructure-as-code management platforms, particularly Harness IaCM, as a way to add centralized orchestration, workspace templates, reusable module registries, policy gates, access controls, audit histories, drift detection, and support for Terraform, OpenTofu, and Terragrunt. The central argument is that production-scale infrastructure requires structured processes and automated guardrails to maintain both delivery speed and compliance rather than relying solely on scripts, conventions, and direct Terraform commands.

Harness AI SRE aims to reduce the 10-to-30-minute coordination phase at the start of incidents by automating repeatable intake tasks through visual, trigger-based runbooks. These workflows can create Jira or ServiceNow tickets, open Slack channels, start Zoom bridges, assign incident fields, page responders, and record actions in an incident timeline using more than 50 integrations. Runbooks can be initiated by monitoring-tool webhooks, manual actions, or incident changes, with rules and conditional branches allowing different handling for alerts based on severity, tags, fields, or thresholds. The platform’s Deploy Change Investigator also links build events, deployment data, merged pull requests, feature-flag changes, and related alerts to identify changes associated with an incident and rank potential causes by confidence. Runbooks may trigger remediation actions such as deployment rollbacks through Harness pipelines, although the approach emphasizes human oversight, with automation handling setup and contextual investigation while responders retain responsibility for resolution.

Traditional FinOps is portrayed as overly reactive, focusing on eliminating visible waste after budget alerts or surprise bills rather than continuously identifying missed savings opportunities. The proposed proactive model treats cloud costs like performance metrics through real-time visibility, automated governance, resource tagging, anomaly detection, rightsizing, lifecycle policies, and ongoing optimization of Reserved Instances and Savings Plans. It also recommends distributing accountability across platform and service teams so that those making engineering decisions own relevant cost targets alongside reliability and delivery objectives. The text argues that preventive controls, such as cost-aware deployment templates and spending reviews in pull requests, can prevent inefficient provisioning before it occurs. Harness Cloud Cost Management is presented as a platform supporting this approach through multi-cloud cost visibility, automated tagging, dynamic budgets, optimization recommendations, policy-driven automation, and integrations with engineering workflows.

An A/B testing framework for software delivery combines traffic allocation, feature flags, targeting, exposure tracking, business and reliability metrics, governance, and promotion or rollback decisions to compare feature variations safely. The approach advocates embedding experimentation directly into CI/CD rather than treating it as a separate post-release process, enabling teams to deploy code with features disabled, progressively expose them to users, and use real-time guardrail metrics to guide rollout decisions. Key architectural practices include separating the experiment control plane from runtime data collection, defining experiment rules and policies as version-controlled code, and connecting validation, staged exposure, measurement, and automated actions within pipeline stages. Effective experiments begin with a clear hypothesis, use one primary outcome metric alongside reliability and user-impact guardrails, allocate traffic gradually, establish minimum sample sizes and stop conditions, and conclude with a documented promotion, pause, rollback, or cleanup decision. The discussion also distinguishes A/B testing, which identifies the better user experience, from canary deployment, which reduces operational risk during version rollout, and emphasizes automation, consistent user bucketing, mutual-exclusion rules, and removal of obsolete flags to prevent experiment debt.

ChainDrop was a self-propagating npm worm reported on August 4, 2026, that compromised hundreds of package names and more than 2,200 versions by exploiting legitimate publishing workflows and stolen credentials. Its malicious preinstall hooks targeted developer workstations and CI/CD systems for GitHub, npm, cloud, Vault, Kubernetes, database, and other credentials, then used compromised publishing access to infect additional packages; researchers also found persistence through editor and AI coding-tool configurations as well as a token-monitoring mechanism that should be removed before credentials are revoked. The incident demonstrates that valid SLSA provenance can accurately confirm that an authorized workflow produced an artifact while failing to establish that its source code or workflow inputs were safe. Recommended defenses include source and workflow governance, independent review, correlation of releases with approved commits and tags, restrictions on install-time scripts, ephemeral least-privilege identities, isolated build runners, artifact policy gates, dependency controls, and runtime monitoring. Harness presents its supply chain security, policy enforcement, artifact management, access-control, and audit capabilities as tools that can support these layered controls, while emphasizing that attestations should be one element of a broader risk decision rather than proof that software is safe.

Harness Pipeline’s Q2 2026 update introduces 20 enhancements focused on flexible execution, governance, Git workflows, and operational visibility. A beta DAG pipeline model lets stages use explicit dependencies for fan-out, fan-in, and parallel workflows, while pipeline chaining supports looping strategies, matrix exclusions are evaluated before execution, barrier cycles are caught during validation, and cron triggers gain AND-based scheduling. Governance additions include AI-assisted OPA policy authoring, enforcement of policies on Git-backed changes, six-month policy evaluation retention, and cloud storage for large evaluation inputs. The release also adds account-wide execution management, configurable template overrides and semantic template labels, a dry-run API for validating Git-based YAML before commits, expanded tag filtering, Git-tag references for pipelines and input sets, webhook and repository-health monitoring, input-set caching, and Git-based YAML support in dynamic stages. Other improvements include multi-line variables and notifications when pipelines pause for required user action.

Harness’s Q2 2026 update introduces 38 enhancements across Continuous Delivery, verification, and GitOps, emphasizing safer progressive releases, AI workload support, and more self-service operations. Kubernetes improvements include staged percentage-based canary rollouts, safer blue-green rollback scaling, expanded dry-run options, more precise Istio routing, and OIDC client-credential access, while AWS, Google Cloud, OCI, and traditional deployment updates add capabilities such as coordinated rollbacks, spot-instance policies, CDK optimization, pre-traffic MIG validation, and Helm charts from Google Artifact Registry. Harness also adds first-class deployment pipelines for AWS and Google AI agent runtimes, applying approval gates, canaries, automated rollback, and policy controls to agent releases. Verification features introduce Git-managed monitored-service configurations, configurable AI Verify windows, per-metric sensitivity, and full CloudWatch Metrics Insights query support. GitOps enhancements include UI and pipeline-based application rollback, multi-namespace applications, selective synchronization, stronger RBAC, squash-and-merge workflows, no-op release-repository updates, AI-assisted troubleshooting, Zero Trust agent communication, and an updated bundled ArgoCD version.

AI systems such as Mythos can identify vulnerabilities far faster than human analysts, but the central challenge for organizations is converting discovery into prioritized, remediated, and deployed fixes. The passage argues that conventional security workflows create large backlogs because triage, developer ownership, remediation, CI/CD deployment, and verification often take days or weeks, meaning increased detection alone may not reduce risk. It proposes a machine-speed response framework built around immediate exposure visibility through software inventories and code analysis, contextual prioritization using exploitability and reachability data, AI-generated and validated remediation pull requests, virtual WAF patching to protect production before code fixes arrive, and automated audit trails. A financial-services example reportedly reduced patch cycles from five days to under two hours through automation. The author frames vulnerability response as a joint security, engineering, and DevOps responsibility and predicts that AI agents will increasingly automate threat monitoring, triage, remediation, and virtual patch deployment. As advanced scanning capabilities become more broadly accessible to attackers, organizations that integrate automated security response into their delivery pipelines are presented as better positioned to manage rapidly emerging threats.

Infrastructure failures often arise during day 2 operations, when manual console changes, incident fixes, and disconnected provisioning, configuration, and deployment tools cause actual environments to drift from Infrastructure as Code declarations. The piece argues that Terraform, Ansible, CI/CD systems, and policy tools typically operate in silos, leaving organizations without end-to-end state reconciliation, consistent governance, or shared operational context. It proposes a unified infrastructure control plane that continuously compares desired and actual state, detects or remediates drift, enforces security and cost policies before changes advance, and preserves ownership and RBAC across the delivery lifecycle. Harness is presented as a platform that can unify these workflows and use AI agents with broad delivery context to assist with governed remediation, decision-making, and automation. The recommended approach is to manage infrastructure as a governed product using the same pipelines, approvals, policy checks, cost estimates, and audit trails applied to application code, making compliance enforceable and reducing operational risk.

Harness refactored its Continuous Integration Test Intelligence service after a flat, denormalized test-result table became inefficient at millions of records, causing repeated storage of hierarchy strings, slow text-based queries, full-scan summaries, payload-dependent API latency, and memory pressure. The new design separates fast API acceptance from asynchronous processing, streams uploads into storage with fixed memory overhead, uses normalized tables with integer foreign keys, incrementally merges report deltas, and maintains pre-aggregated counters for constant-time summary retrieval. It also uses hybrid storage, retaining small reports as compressed database blobs and placing large reports in columnar object-storage files queried through an embedded analytical engine, while workers scale horizontally through queues and distributed coordination. Load testing exposed further bottlenecks in indexed database writes, ID lookups, and autoscaling speed, leading to staging tables, batch-oriented lookup patterns, and more responsive scaling. The migration preserved existing API contracts and used feature flags to write to both systems before progressively shifting reads, while AI-assisted design iterations helped examine edge cases alongside planned unit and integration testing.

Harness AI Evals is presented as a platform for testing non-deterministic AI agents through a unified evaluation system that uses the same datasets, metrics, and scoring logic before deployment and in production. Users define evaluations through targets such as prompts or endpoints, versioned datasets of test cases, quality metrics including deterministic checks, LLM-as-a-judge rubrics, safety scoring, and multi-step trajectory analysis, plus thresholds that can block or advise on releases. Evaluations can be grouped into suites and integrated as native CI/CD pipeline quality gates, allowing regressions to fail builds without custom scripts. An example customer-support response demonstrates how an answer can sound polite while failing relevance-related metrics, preventing deployment. The platform also emphasizes enterprise features including role-based access, policy governance, audit trails, secrets management, SSO, data residency, versioned registries, and token-cost tracking, while planned capabilities include Git-backed configurations, production-trace observability, automated dataset creation, drift detection, rollback, prebuilt suites, and human annotation workflows.

Application releases frequently depend on database schema changes such as new tables, columns, indexes, constraints, and reference data, but managing these changes separately from application deployments can create coordination delays, compatibility failures, limited visibility, and difficult rollbacks. The article argues that Harness Continuous Delivery and Harness Database DevOps address these issues by incorporating version-controlled database migrations into a unified CI/CD pipeline, where schema changes are applied before a Kubernetes application deployment and followed by health verification. If failures occur, automated rollback procedures can restore the prior application version, while centralized pipeline controls improve migration traceability, governance, compliance, and promotion across development, staging, and production environments. By bringing developers, database teams, and platform engineers into one automated release workflow, the approach aims to accelerate delivery while reducing operational risk and improving release consistency.

AI-driven software development can accelerate delivery but can also amplify weak testing, documentation, and operational practices, creating a need for disciplined controls alongside agent adoption. Based on Harness’s six-month redesign of its software development lifecycle, the proposed framework centers on keeping product, technical, UI, and test specifications versioned within code repositories; assigning narrowly scoped agents only to tasks requiring reasoning rather than using them for conventional automation; applying layered verification across interfaces, APIs, agent output quality, data, continuous efficacy, and production feedback; and embedding operational readiness reviews and ongoing monitoring throughout development and release processes. Harness reports that this approach increased features shipped by 23%, reduced incidents by 44%, and produced a 45% return on AI investment, arguing that organizations can obtain AI-assisted speed without sacrificing reliability when they treat agentic systems as critical systems requiring explicit context, governance, testing, and operational rigor.

AI-driven coding tools are accelerating software delivery but also increasing the volume of code and potential vulnerabilities, widening an existing gap between development speed and application security teams’ ability to respond. Traditional SAST programs often struggle because developers distrust false-positive-heavy findings, vulnerabilities remain unresolved, business logic and authorization flaws evade pattern-based scanning, and tool maintenance limits coverage across repositories and pipelines. The discussion distinguishes LLM-native security testing, which can provide contextual reasoning and identify novel flaws but may be inconsistent or hallucinate, from AI-assisted SAST, which enhances deterministic scanners with more repeatable and auditable results but may offer only incremental detection gains. It argues that AI cannot eliminate false positives, replace conventional SAST entirely, or remove the need for human validation, and recommends that buyers assess tools’ ability to secure AI-generated code, reduce noise, detect business logic issues, improve remediation, and explain findings. A hybrid model combining deterministic governance and repeatability with LLM-based contextual analysis is presented as the likely path forward, with teams encouraged to shift controls earlier into coding workflows, prioritize fixes over finding volume, and improve developer experience to make security effective at modern delivery speeds.

Harness Resilience Testing has updated its documentation by embedding the Chaos Hub catalog and adding a Prompt Library for Harness MCP, aiming to help teams identify and address reliability weaknesses before production incidents occur. The Chaos Hub integration lets users browse and filter more than 200 fault, probe, and action templates across environments such as Kubernetes, cloud platforms, Linux, and Windows directly within the documentation. The Prompt Library provides configurable natural-language prompts that can be used in IDEs or Claude through MCP for common workflows, including auditing resilience-test coverage, ranking services by unmitigated risk and recommended experiments, and designing and running pod-failure experiments. These additions complement the platform’s controlled fault-injection capabilities and AI-assisted analysis, while encouraging teams to assess services with limited or no chaos-testing history and expand automated resilience practices.

A mature disaster recovery program extends beyond occasional tests by using risk-aligned schedules, automation, measurable remediation, and continuous improvement to make recovery more reliable, auditable, and sustainable. Organizations can progress from reactive, ad hoc exercises to continuous resilience practices by testing critical services more frequently, maintaining living runbooks and incident knowledge bases, assigning and verifying corrective actions, and sharing lessons across teams. Key technical practices include recovery-as-code, automated backup restores and integrity checks, selective chaos engineering, observability, and orchestration for hybrid or multicloud failovers. Programs should also coordinate with security, compliance, legal, and third-party providers to meet requirements such as ISO 22301, NIST, HIPAA, and PCI DSS while protecting test data. Effectiveness should be evaluated through trends in recovery time versus RTO, data loss versus RPO, automation coverage, remediation closure and recurrence, and customer-impact indicators, with each metric tied to concrete improvements. Harness Resilience Testing is presented as a platform that combines disaster recovery, chaos, and load testing within CI/CD pipelines to help teams consolidate these practices.

Cloud cost visibility often becomes challenging as organizations scale, transitioning from manageable scenarios with clear tracking to complex environments where traditional methods fail. Initial systems and processes, effective for smaller teams and predictable workloads, struggle in fast-growing, multi-cloud environments. Traditional approaches to cloud cost management, akin to those used for on-prem infrastructure, are insufficient due to the dynamic nature of cloud environments where infrastructure is provisioned via API calls and workloads scale automatically. Cost allocation complexities arise as shared services support multiple teams, and multi-cloud environments fragment visibility due to varied pricing models and billing practices. Reactive reporting is inadequate, as it often fails to identify the root causes of cost spikes in a timely manner. To address these issues, cost visibility should be integrated into engineering workflows, employing automated allocation, context-aware anomaly detection, and proactive governance. Harness Cloud Cost Management exemplifies this approach by providing unified cost visibility across multiple cloud platforms, automating allocation based on real ownership, and linking cost changes to engineering activities, fostering a cost-aware engineering culture.

Artifact management is increasingly challenged by the rapid growth of AI-generated software artifacts, such as container images and packages, which complicates cleanup processes in registries. The difficulty lies in balancing the need to control storage costs and avoid removing essential artifacts still in use, a challenge exacerbated by traditional cleanup rules that are not suited for the speed and volume of AI-driven software delivery. Harness Artifact Registry addresses these challenges with Lifecycle Rules that integrate metadata across the software development lifecycle to intelligently protect deployed artifacts from deletion without requiring additional rules. It offers features like dry-run previews, soft-delete with recovery options, and an attachment model that allows rules to be selectively applied across different scopes, ensuring a policy-driven, predictable, and safe approach to artifact lifecycle management. This system enables teams to automate cleanup without risking operational disruptions, providing a scalable solution for managing artifact registries as software delivery accelerates.

Config Management, now available in beta, offers a governed runtime control plane that separates runtime configuration from application deployments, allowing for instantaneous delivery of configuration changes across various environments without the need for code redeployments. This tool enables teams to manage configurations across development, staging, and production environments, facilitating immediate updates to runtime values such as retry limits and timeout thresholds through the Harness SDKs. By providing a centralized platform to create, organize, and govern runtime configurations, Config Management helps teams optimize application behavior, improve operational performance, and iterate through experimentation, while maintaining enterprise-grade governance and control. The platform supports managing AI-specific configurations, offering the same runtime control over AI behavior as traditional software, and is an integral part of the Harness platform's capabilities for progressively releasing features and monitoring production health.

Harness has introduced a significant update in July, unveiling 71 features that enhance the lifecycle management of AI agents, aligning with the increased productivity AI is bringing to software development. The updates include a comprehensive framework, Harness Agent DLC, that allows teams to manage AI agents through a unified platform used for traditional services, ensuring cohesive build, test, deploy, operate, and governance practices. A key feature, Harness CLI 3.0, offers a standardized command-line interface for both humans and AI agents, streamlining workflows across different modules. Additionally, new tools provide insights into AI spending and its outcomes, allowing leaders to trace AI token usage back to specific developments. The release also emphasizes security and governance, with tools like AI Bill of Materials that catalog AI components for better oversight and risk management. Continuous Delivery enhancements include Kubernetes canary deployments and a refined rollback process, while the platform's infrastructure as code management now integrates Ansible for a more seamless configuration workflow. These features reflect Harness's commitment to integrating AI into the software development lifecycle while maintaining control and visibility over AI-driven processes.