Discover how Harness Software Delivery Agent closes the gap between coding and production with AI-powered reviews, testing, security, and deployment automation.
TL;DR
- AI coding assistants sped up writing code, but review, build, security and deployment still run at human speed.
- The Harness Software Delivery Agent automates that outer loop with shared context, expert agents and worker agents under one set of permissions and one audit trail.
- A Trust Gate checks whether AI-authored changes earn their green build or weaken their own tests to get it.
AI coding assistants such as Claude Code, Cursor, and GitHub Copilot have largely solved the inner loop: writing code faster. The outer loop, everything after a pull request lands, has not kept pace. Code is written at machine speed and still reviewed, tested, and shipped at human speed. The Software Delivery Agent from Harness is built to close that gap. In this on-demand webinar, Beyond the Coding Agent: How the Software Delivery Agent Closes the Gap to Production,
CJ Smith, Senior Sales Engineer shows how, with live demos.
Why is the outer loop the real software delivery bottleneck?
Planning and coding have sped up substantially over the past two years. Review, build, test, security, and deployment have not. Teams are no longer limited by how fast code is written. They are limited by how fast it can be delivered safely, and faster delivery of unreviewed, unverified code is not progress.
What are the four layers of the software delivery agent?
The Software Delivery Agent is one platform with four layers:
- Interface: a chat front end developers already use, for questions like "which pipelines exist?" or "why did this one fail?"
- Context: a knowledge graph of the organization's systems.
- Reasoning: expert agents for software delivery, security testing, runtime protection, and cost that investigate and recommend.
- Execution: worker agents that do the work, from a prebuilt marketplace or built custom.
Because all four layers share one platform, they share one set of permissions and one audit trail. See how the worker-agent marketplace works.
How does the SDLC knowledge graph give every agent the same context?
The graph maps ownership, dependencies, monitoring, code quality and on-call routing for every service. That is the institutional knowledge a new engineer spends months absorbing. Every agent reads the same map, so they answer specific questions about your systems instead of guessing. The platform connects to your existing tools and is reachable from the IDE, CLI, Claude, or Cursor through an MCP server.
How does Trust Gate stop AI agents from gaming their own checks?
Reward hacking is when a coding agent optimizes for a green build by weakening a check instead of fixing the code. A pipeline that only watches pass or fail cannot tell the difference. The Trust Gate worker agent evaluates whether a change does what it claims without weakening a test or scan. In the demo:
- PR 1: a clean, honestly tested change. Cleared to merge automatically.
- PR 2: rewrote a test assertion to match a typo it introduced and disabled another test by labeling it flaky. Flagged as reward hacking and blocked.
- PR 3: removed tests legitimately because the code they covered was deleted. Trust Gate agreed, but a deterministic policy still required human sign-off.
AI can raise the bar on what gets approved automatically. It cannot lower it. Every approval and rejection is recorded, so the audit trail comes from the pipeline itself.
What are golden paths, and how do they end pipeline sprawl?
Golden paths are centrally governed pipeline templates with security and compliance controls built in. Morningstar consolidated roughly 36,000 individually scripted pipelines into 50 templates. Templates live as a Pipeline as Code YAML in your own Git repository, so adoption is not a rip-and-replace.
How does Policy as Code enforce guardrails developers cannot script around?
In the demo, an attempt to delete a required production-approval step was blocked by policy evaluation before it could be saved. Approval gates connect to ServiceNow or Jira through prebuilt, scriptless steps, so change records look the same across teams. Deluxe builds its security and policy controls into shared templates so every team inherits them.
How does the software delivery agent automate production deployments?
- Database schema changes ship with application code and roll back with it.
- Canary and blue/green deployments are set as a percentage, not custom scripts.
- The canary step compares error rates through your observability tools and rolls back automatically if the canary looks unhealthy.
How do build, test, and security intelligence cut pipeline time?
- Build and cache intelligence rebuilds only what changed.
- Test intelligence runs only relevant tests. In the live demo it saved more than an hour on one run.
- Security deduplicates findings across scanners and surfaces only what is new in the current scan.
- AI remediation researches the vulnerability, writes a fix, and opens a pull request.
What results are customers seeing?
- Citi: release time cut from hours or days to about 7 minutes from build to production
- United Airlines: 75% faster deployments.
- Ancestry: about 80% less new-system onboarding effort, 50% fewer deployment-related production outages and 3x deployment velocity.
