"As AI accelerates code production, it is widening a bottleneck in the far larger "after-code" phase of software development - the testing, security checks, and deployment work that still consumes nearly 70% of engineering time. Harness's tools help automate this sprawling, error-prone layer, even as enterprises grapple with rising AI code volume and the risks of shipping even a single line of faulty software into production systems."
"Harness uses AI agents to automate functions like testing, verification, security, and governance. It is built on a software delivery knowledge graph that maps code changes, services, deployments, tests, environments, incidents, policies, and costs. The knowledge graph helps differentiate Harness from other AI platforms, Bansal said, because it gives the system a deep understanding of each customer's software delivery processes and architecture."
Harness, founded in 2017 by Jyoti Bansal, raised $240 million in a Series E that values the company at $5.5 billion post-money. The round includes a $200 million primary investment led by Goldman Sachs and a $40 million tender offer designed to provide liquidity to long-term employees. The valuation represents a 49% increase from $3.7 billion in April 2022, bringing total equity raised to $570 million. As AI speeds code production, testing, security, and deployment—the “after-code” phase consuming nearly 70% of engineering time—Harness uses AI agents and a software delivery knowledge graph to automate verification, governance, and pipeline generation.
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