Senior Enterprise Account Executive

Harness and Kong Inc. have expanded their partnership to provide comprehensive API and AI security solutions. The collaboration extends beyond their existing API Gateway integration to include Kong AI Gateway, addressing security challenges in AI-driven architectures and autonomous agents. According to a 2025 report, 62% of enterprises lack visibility into their LLM usage, whilst 74% believe AI sprawl will surpass API sprawl in terms of risk. The expanded solution offers AI discovery, automatically inventorying AI assets routed through Kong AI Gateway, and AI protection through real-time behavioural analysis to detect threats like prompt injection and data exfiltration. The integration builds on the companies' existing API security partnership, applying similar security depth to AI infrastructure. Both the API Gateway and AI Gateway integrations are now generally available to joint customers.

Harness launches Agent DLC - CI/CD for AI agents. Harness Agent DLC brings eval gates, deployment governance, security scanning, and tracing for AI agents into existing CI/CD pipelines - addressing the production gap where 92% of agentic AI pilots still aren't shipping to production. Only 8% of organizations have agentic AI in production. That statistic, cited by Gartner and widely repeated across the industry this week, says more about developer tooling than it does about model quality. The models work. What is broken is the delivery pipeline around them - the testing, the governance, the monitoring, and the incident response. On July 21, Harness announced Agent DLC, an extension of its CI/CD platform that treats AI agents with the same pipeline discipline as conventional software. The release addresses a structural gap: in a March 2026 survey, 78% of enterprises reported active AI agent pilots, but fewer than 15% of those pilots had reached production. Gartner now predicts that 40% or more of agentic AI projects will be canceled outright by 2027 - not deprecated, canceled. The problem is not that agents cannot produce useful output. It is that they have been shipping outside the normal release process - fast, informal, and without the quality gates, rollback paths, and observability that production software requires. Harness Agent DLC attempts to close that gap with five capabilities designed to fit into existing CI/CD pipelines. AI Evals - quality gates for non-deterministic systems. Standard continuous delivery assumes deterministic software: the same inputs produce the same outputs. An AI agent breaks that assumption. The same prompt with the same tools can produce different responses across runs, making traditional canary analysis meaningless. You cannot diff an agent's behaviour the way you diff code. AI Evals replaces output comparison with quality measurement. You define a dataset of representative inputs, configure scoring functions for correctness, safety, and performance - each normalized on a 0.0-1.0 scale - and set a pass threshold. Harness wires this into the pipeline as a stage, the same way a lint check or test suite runs. If the eval score drops below threshold after a model change or prompt update, the deploy stops. This is the missing link between manual testing and silent production regressions. For teams already running evals in notebooks or ad-hoc scripts, having them as a pipeline gate changes the workflow from optional check to enforced standard. Agent Deployments - managed runtimes with canary releases. Agent Deployments adds out-of-the-box pipeline steps for Amazon Bedrock AgentCore and Google Agent Runtime, the two most common managed agent hosting services. The deployment model includes canary releases - route a percentage of traffic to a new agent version before full rollout - and Open Policy Agent guardrails that enforce operational boundaries at deploy time. For teams not using Harness, the pattern itself is worth adopting: treat an agent deployment as a staged rollout, not a file copy. The same approval gates and rollback procedures that protect microservice deployments should protect agent deployments, yet most teams skip them. AI Configs - decouple model and prompt changes from binary. AI Configs separates model selection and prompt content from the agent binary, backed by Harness's existing feature flag infrastructure. You can swap the underlying model or adjust a prompt at runtime without a full redeploy. If a prompt change degrades quality, roll it back from the config layer rather than cutting a new release. This mirrors what platform teams have done for years with feature flags and configuration management. Applying the same discipline to AI configuration means prompt changes become auditable, reversible, and separable from code deploys - something that is rarely true in current agent workflows. Agent Security - from bill of materials to continuous firewall. Agent Security introduces automated vulnerability scanning specific to agent architectures. It generates a bill of materials for every agent - covering base models, plugins, skills, and dependencies - and runs adversarial input tests before deployment to probe for prompt injection, jailbreaking, and tool misuse vulnerabilities. In production, a continuous firewall monitors agent inputs and outputs for policy violations. For regulated industries where agent decisions have compliance implications, this layer is essential. For everyone else, the bill of materials alone is a significant step forward. Most teams today cannot answer the question "what models and plugins does this agent depend on?" with any confidence. AgentTrace - decision-path observability for debugging. AgentTrace records the full decision path for every agent run: which tools were called, the latency at each step, the prompts and model outputs at every turn. The practically useful feature is the ability to export production failures into eval cases with one click, closing the loop between what breaks in production and what gets tested in CI. Harness is open-sourcing the foundational components - the harness-sdk and harness-evals libraries - under Apache 2.0. These work with any OpenTelemetry backend, meaning the observability standard itself is not locked to the Harness platform. If you use OTel today for your existing services, you can plug in AgentTrace components without migrating your observability stack. What this means for the agent delivery pipeline. Agent DLC does not introduce concepts that are new to platform engineering. Eval gates, staged rollouts, config decoupling, security scanning, and decision-path tracing all have equivalents in conventional software delivery. What is new is applying them to AI agents as a unified pipeline concern rather than bolting them on after the fact. The broader takeaway is structural. The reason most agent pilots do not reach production is not a failure of model intelligence - it is a failure of delivery discipline. Teams that treat agents as experimental scripts skip the infrastructure investments, governance processes, and ownership structures that would accompany a normal service release. Agent DLC gives teams a forcing function: when your agent goes through the same change record, quality gates, and approval chain as the rest of your software, it becomes harder to treat it as still experimental. Getting started. If your team is already on Harness, Agent DLC features are rolling out to customers now. For teams using other CI/CD platforms, the open-source harness-evals library provides the eval gate component independently, compatible with any OpenTelemetry backend. Start with observability - most production agent failures are detected by users before they are detected by dashboards, and AgentTrace addresses that gap directly. For related context, see the earlier piece on why AI agents in DevOps are only as good as the platform beneath them, which argues that the same engineering fundamentals - tests, telemetry, rollback paths - determine whether AI automation helps or hurts, regardless of the tooling layer on top. Related What I Do Related What I Do. These What I Do pages are matched from the subject matter of this article, creating a cleaner path from educational content to implementation work.

Harness and Kong expand strategic partnership. July 23, 2026 Harness and Kong announced an expansion of their strategic partnership to address the growing security challenges posed by AI-driven architectures, autonomous agents, and Model Context Protocol (MCP) deployments. The two companies are extending their joint solution from Kong API Gateway to also include Kong AI Gateway, bringing Harness's AI security intelligence directly into the AI infrastructure layer and enabling enterprises to discover, monitor, and protect every agent, AI asset, LLM-powered service, and MCP-connected workflow that traverses it. Harness and Kong have been jointly trusted by enterprises to deliver best-in-class API security for years. The existing Harness and Kong API Gateway integration provides: - Comprehensive API traffic visibility and behavioral analysis across all Kong-managed services - Real-time detection and blocking of API threats, including OWASP API Security Top 10 risks, credential stuffing attacks, and business logic abuse - Continuous sensitive data tracking to identify PII exposure and regulatory risk - Zero-friction deployment alongside existing Kong configurations This new offering of the AI Gateway solution applies the same level of security depth to AI infrastructure, ensuring that security teams are not left behind as their organizations adopt AI and agentic operations. "Our partnership with Harness has given joint customers production-grade API security that works with the way they build, not against it," said Ken Kim, Senior Vice President, Business Development at Kong Inc. "Extending to include Kong AI Gateway is a natural next step. The same enterprises are now moving AI into production through our gateway and need the same depth of visibility and control they've come to rely on for their APIs for all AI traffic types including LLM, MCP, and A2A. That's exactly what this delivers and is crucial for organizations scaling in the agentic era." The new Harness and Kong AI Gateway integration directly tackles these challenges across two critical domains: AI discovery and AI protection. - AI Discovery: Harness automatically inventories every AI asset, API, MCP server, tool, prompt, and resource routed through Kong AI Gateway - providing security teams with a continuously updated catalog of their AI attack surface. No manual documentation. No blind spots. - AI Protection: Harness applies behavioral analysis and anomaly detection to AI traffic in real time, identifying prompt injection attacks, data exfiltration through AI responses, jailbreaking, malicious code in prompts, and other AI-specific threats. Enterprises gain the same depth of observability and protection for their agents and AI workloads that they already rely on for traditional APIs, with full prompt and response details available for incident investigation and inline policy enforcement through Kong AI Gateway. "Shadow AI has become the defining security blind spot for enterprises today. Traditional tools were built for static code and predictable systems, not for adaptive AI models, agent-to-agent communication, and MCP-connected workflows that evolve continuously," said Rahul Sood, GM of Application Security at Harness. "This integration of Harness AI Security with Kong puts security intelligence directly into the connectivity layer where AI traffic flows. Joint customers now have the visibility and control they need to move fast without losing sight of what's happening across their AI infrastructure." The Harness and Kong API Gateway integration is generally available now for all joint customers. The Kong AI Gateway integration, including AI Discovery and AI Protection, is also generally available now. Industry news. July 23, 2026 Azul announced that it will deliver monthly Critical Security Patch Updates (CSPUs) for Java Long-Term Support (LTS) versions for both Azul Core and Azul Prime, starting in August 2026. July 23, 2026 Prismatic announced native large data sync, a set of new capabilities that enables teams to move high volumes of customer data through the same integration platform they already use to build, deploy, monitor, and manage customer-facing integrations. July 23, 2026 Harness and Kong announced an expansion of their strategic partnership to address the growing security challenges posed by AI-driven architectures, autonomous agents, and Model Context Protocol (MCP) deployments. July 22, 2026 Sauce Labs introduced AURA, its AI-Unified Release Assurance platform, to close that gap. July 22, 2026 Harness announced the Harness AppSec Alliance, a strategic partner ecosystem that brings together application security, API and Agent security capabilities on a single platform. July 22, 2026 Mirantis announced that both k0s, its lightweight single-binary Kubernetes distribution, and k0rdent, its open-source distributed container management platform, have achieved CNCF Certified Kubernetes AI Conformance at Kubernetes v1.35. July 21, 2026 Google introduced Gemini 3.6 Flash, Gemini 3.5 Flash-Lite, and Gemini 3.5 Flash Cyber to help developers and customers build AI agents in production with higher token efficiency, lower latency, and reliable performance. July 21, 2026 Harness is extending its platform to cover the full AI Agent Development Lifecycle (DLC), giving enterprises a single set of pipelines and controls to build, test, deploy, and run agents the same way they already ship everything else. July 21, 2026 BellSoft announced the general availability of a new hardened builder image for Paketo Buildpacks(TM). July 20, 2026 Testlio unveiled the next evolution of its AI-driven testing platform, LeoCore(TM). July 20, 2026 Agent Island released v1.7.1. July 17, 2026 GitLab(link is external) released GitLab 19.2. As AI generates more code, dependencies, and change than developers can keep up with, GitLab 19.2 brings agentic automation to clear that load. July 16, 2026 Progress Software(link is external) announced that its Progress Chef(link is external) platform now delivers enterprise lifecycle management and configuration capabilities for NVIDIA DGX Spark, enabling IT teams to securely provision, monitor and manage the desktop AI supercomputer at scale. July 16, 2026 Atlassian Corporation announced new capabilities in Jira to advance AI-native software development for every engineering organization. July 16, 2026 Intruder announced the launch of AI Pentesting for web applications, providing on-demand penetration testing.