Harness Launches Autonomous Worker Agents for Software Delivery

Build and safely run AI agents for software delivery, with a curated Marketplace featuring Harness Managed and Community-authored agents.

SAN FRANCISCO, June 30, 2026 /PRNewswire/ -- Harness, the AI Software Delivery Platform^TM company, today launched Autonomous Worker Agents for software delivery: the platform for enterprises to build and safely run AI agents that handle the work between writing code and shipping it to production.

Software delivery has moved through phases. First, people did the work by hand. Then they wrote scripts for individual jobs like deployment. Most recently, they connected those jobs into automated pipelines that follow fixed instructions, which is what Harness has run for large enterprises for years. Worker Agents are the next phase. Every step in the pipeline can now run as a reasoning agent rather than a fixed script, with the context, governance, sandboxing, and audit trails that enterprises need to trust agents in production.

Harness Managed Agents are available today, and any team can customize them or build their own. A new Harness Agent Marketplace makes it easy to find, use, and share them.

"AI now writes the code. Harness ships it," said Jyoti Bansal, co-founder and CEO of Harness. "Autonomous Worker Agents are how enterprises build and safely run AI for everything after code: building, testing, securing, deploying, operating. All of it runs on the same pipelines that already ship our customers' software, inside their own network boundary. The governance, the audit trail, and the security posture are already there. Worker Agents inherit it all from day one."

The controls that keep agents safe in production

Autonomous Worker Agents execute as pipeline-native steps in Harness and are governed by the same controls enterprises already use for human deployments. When agents invoke an LLM, the prompt and context are passed through an LLM Gateway that validates the request against policy while maintaining audit trails.

These controls make Autonomous Worker Agents safe to run in production:

• Sandboxing: Agents run in isolated containers with restricted file and network access. An agent that produces a malicious command has nowhere to send data.
• Scoped credentials: Each agent has its own identity and the specific set of permissions assigned to it, the same way an employee does. An agent can only take the actions those permissions allow, no matter who triggers it or what its prompt says.
• Policy enforcement: The same policies that gate human deployments gate agents. A policy can keep agents off non-approved models out of production pipelines.
• Audit trails: Every agent action is recorded under a distinct AI identity, with full provenance – what triggered the agent, what it did, and the outcome.
• Cost tracking: Token spend is surfaced per agent and per pipeline.
• Chaining: Agents compose into multi-step workflows, passing output from one to the next.

Easy to build, governed by your policies

Building an Autonomous Worker Agent uses the same agent-file format that has become standard across the industry. Save it to a single file, commit it to your repository, and the agent is live, governed, and available across your organization. Teams that would rather not write the file can use Harness AI to generate the agent for them. Either way, the agent runs as a governed pipeline step with the same controls, audit trail, and policy enforcement as everything else.

"We built a Kubernetes troubleshooting agent that evolved from simply reading logs to actually troubleshooting issues very quickly. This agent will be rolled out at the org level," said John Jones, Director of Cloud Infrastructure at Verint Systems. "Having the agents inside the pipelines without needing to finagle calls out to other tools is extremely helpful. The agent will benefit more than 200 members of our operations team and ~1,000 developers. It only took us four days to learn and build a production-ready AI agent that will help us with our most common and time-consuming task of troubleshooting pipeline failures."

Once it runs, the agent has your organization's full context. It reasons using the Harness Software Delivery Knowledge Graph, a connected map of your services, pipelines, deployments, infrastructure, incidents, and security findings. An agent assessing a vulnerability knows which services are affected and who owns them. A deployment agent knows which services depend on the one being deployed. The result is a response built for your specific environment, not a generic fix that only looks right.

Agents also meet you where you work. Through the Harness MCP Server, a developer in Cursor, Claude Code, or another tool can assign a task to a Worker Agent and have it run in Harness, with the result returned to wherever it was triggered. Wherever an agent runs, it runs under your organization's policies and is governed the same way as every other step in your pipeline.

Harness-built agents, ready to use today

Harness has pre-built Autonomous Worker Agents that handle the repetitive, time-consuming work that slows teams down across the delivery lifecycle. Here are a few of the agents available today, with more added regularly:

• Autofix reads build logs, identifies the root cause of a build failure, commits a fix to the PR branch, and re-triggers builds until it passes.
• Code Review reviews PR diffs for code quality, security issues, and test coverage.
• Code Coverage identifies untested lines and generates tests to close coverage gaps.
• Feature Flag Cleanup detects stale flags and validates safe removal.
• Manifest Remediator analyzes failed Kubernetes deployments and fixes manifest issues.
• IaCM Remediation fixes configuration drift, security findings, and cloud cost issues by editing infrastructure configurations.

The Harness Agent Marketplace

The Harness Agent Marketplace is a shared catalog where Worker Agents are published and reused across an organization and the broader Harness community. Teams can adopt an existing agent rather than build their own, and contribute the agents they build back to the catalog.

It has three tiers:

• Harness Managed: Built, maintained, and SLA-backed by Harness.
• Harness Certified: Built by partners, reviewed and certified by the Harness engineering and security teams.
• Community: Published by the broader Harness community. Organizations can use out-of-the-box policies to control which community agents run in production.

Every agent in the Marketplace can be forked. A team can create its own agent by cloning an existing agent and adjusting the prompt, tools, or triggers to fit its environment. The agent one team builds to solve a problem becomes the starting point for the next team that hits the same roadblock.

"We built RiskSentinel, a Harness Autonomous Worker Agent, to demonstrate that governed AI can move beyond identifying security issues to safely remediate them while maintaining enterprise controls, auditability, and compliance," said Ratna Devarapalli, Director IT, United Airlines. "When building with Harness, what stood out most was how intuitive the experience was — it enabled our team to move from an initial idea to a production-ready agent in just four days, allowing us to focus on solving a real enterprise challenge rather than the underlying platform. That combination of developer experience and enterprise-ready capabilities is what will enable organizations to confidently scale AI across software delivery."

Bring your own model

Autonomous Worker Agents work with multiple LLM providers, including Anthropic via AWS Bedrock, as well as directly with Anthropic and OpenAI. Customers can switch models per agent, per environment, or per pipeline without rewriting the agent.

Availability

Autonomous Worker Agents and the Harness Agent Marketplace are now generally available to all Harness customers. For more information, visit https://harness.io/platform/worker-agents.

About Harness

Harness is the AI Software Delivery Platform™ company, enabling engineering teams to build, test, and deliver software faster and more securely. Powered by Harness AI and the Software Delivery Knowledge Graph, the platform brings intelligent automation to every stage of the software delivery lifecycle after code — removing toil and freeing developers from manual, repetitive work. Companies like United Airlines, Morningstar, and Choice Hotels use Harness to accelerate releases by up to 75%, cut cloud costs by 60%, and achieve 10x efficiency across DevOps. Based in San Francisco, Harness is backed by Goldman Sachs, Menlo Ventures, IVP, Unusual Ventures, and Citi Ventures.

SOURCE Harness