Key takeaway: The Harness MCP Server now connects directly inside Google Antigravity. Developers can link Harness in under two minutes and give the agent structured, real-time access to their pipelines, execution history, services, environments, and policies, without leaving the editor. What makes it reliable isn't the connection itself. It's the Harness Software Delivery Knowledge Graph underneath, which gives the agent the context to act accurately, fast, and within your guardrails.
AI has made the inner loop faster than ever. Inside Antigravity, you can write, refactor, and test code in seconds. But the moment a change needs to be built, deployed, or debugged in production, you leave the editor entirely, back to juggling pipelines, approvals, scan results, and failed runs across a half-dozen browser tabs. That gap between fast code and slow delivery is the part AI hasn't fixed yet.
The Harness MCP integration closes that gap. Connect once, and Antigravity gains direct access to your Harness delivery environment. The agent now understands your delivery system the same way it understands your codebase. So you can ask it to list pipelines, explain a failure, or trigger a deployment, and it acts on live Harness context instead of generic knowledge.
Connect Antigravity to Harness in a Few Steps
There's no YAML to write and no manual server config. You generate a Personal Access Token in Harness, open Antigravity's Customizations panel, add the Harness MCP server, and paste the token. That's the entire setup.
- Generate a PAT. In Harness, go to Account Settings → Personal Access Tokens and scope the token to the org, project, and pipelines you want the agent to reach.
- Open Customizations. In Antigravity, go to Settings → Customizations to configure default behaviors, skills, and MCP server connections.
- Add the Harness MCP server. Click + MCP Servers, search Harness, select it, and paste your PAT.
- Start building. The agent now operates with your Harness account, org, and project context. Describe what you need and it acts on real pipeline and execution data.
Settings → Customizations → Add MCP Servers, search “Harness,” connect, done.
All The Software Delivery Use Cases Within Antigravity
Once Harness is connected, you interact with your delivery system the same way you interact with your code, in plain language, from the same window.
Ask it what's in your delivery environment
Start simple: "Can you list pipelines in <Your Project> project in the default org in my Harness account?" The agent resolves the project identifier, pages through every pipeline, and returns a structured report (names, identifiers, creation times, descriptions, and tags) with links straight back to the Harness UI.
Drill into execution history
From there, ask about recent activity: "List out my recent executions in this project." The agent reads the execution history, converts raw timestamps and durations into something readable, and lays out every run, including the one that came back ApprovalRejected, so you can see exactly what happened and when.
Triggering a deployment, with a human in the loop
This is where most "AI in delivery" stories get nervous, and where the design matters most. When you ask the agent to run something, it doesn't just act. It shows you the exact tool it wants to call and the arguments it will send, then waits for your approval.
Approve it, and the run goes through. The agent triggers the pipeline using the Harness run action and returns the live execution (pipeline ID, status, trigger type, and a link to the execution in Harness), so you can follow it from the same chat.
This Is Not AI Without Guardrails
The natural question, once an agent can trigger pipelines, what stops it from doing something it shouldn't? The same controls that govern everything else in Harness.
Why Context Beats Raw API Access
MCP lets a model call external tools by reading API descriptions and deciding which to invoke. That flexibility is useful, but when an agent needs to reason across an entire delivery lifecycle (CI, CD, security scans, approvals, environments, cost signals), raw API access creates a reliability problem. The agent has to discover which endpoints exist, call them in the right order, paginate correctly, and infer how fields relate across systems. Every inferred join is a place to guess. Guessing is where hallucinations happen.
The Harness Software Delivery Knowledge Graph removes the guesswork. It's a purpose-built model of everything that happens after code is written (builds, test runs, deployments, approvals, scans, environment states, feature flags, infrastructure changes, cost signals, and rollbacks) represented as a connected, typed, semantically annotated graph. Every field carries metadata telling the agent how to use it, and relationships between entities are explicitly declared, not inferred.
This is the difference between an agent that can access your delivery system and one that understands it.
When Antigravity connects to Harness via MCP, it isn't handed a list of endpoints. It gets a structured model of your delivery organization, where relationships are known, data types are enforced, and the agent can construct precise queries rather than guessing at field semantics. The same controls apply structurally, too: an approval gate isn't an optional step the agent might skip; it's a typed relationship with state. The agent can't promote past a gate that hasn't cleared, because the graph reflects that clearly. Speed and governance aren't a tradeoff; they coexist by design.
Software Delivery Context At Your Fingertips
If you're already a Harness customer, you're a couple of minutes away from having the software delivery control in Antigravity. New to Harness? Sign up for free and connect from day one. For enterprise onboarding and design-partner access, contact your Harness account team.
The Harness connection gives the agent the ability to act in your delivery system. The Knowledge Graph gives it the understanding to act well. Together, that's what reliable AI in software delivery actually looks like, now available wherever you build, including inside Antigravity.
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Rohan is the Product Lead for Harness AI, driving the future of AI-native DevOps.
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Chinmay Gaikwad is an expert on making complex technologies - such as cloud-native solutions, Kubernetes, application security, and CI/CD pipelines - accessible and engaging for both developers and business decision-makers.
