Antigravity in Practice: Multi-Agent Pipelines for Mid-Market Clients

Why this article now

Antigravity finally graduated from the lab in 2026. The NotebookLM 3.5 integration made it visible in a mainstream product for the first time, and Google has since shipped an Antigravity SDK layer for third-party pipelines.

We've been running Antigravity in production for three weeks across two mid-market projects — a research pipeline for a trade publisher and a reporting workflow for a B2B service provider. This article is the CTO view from those projects.

What Antigravity is, conceptually

Antigravity is not an LLM. It's an orchestration layer that chains multiple agents into a pipeline, carries state between steps and handles failure modes. The right comparison isn't Claude or GPT — it's LangGraph, AutoGen or Temporal with agent adapters.

What makes Antigravity stand out:

• Native tool use over MCP — pipelines can embed MCP servers as steps
• Memory with citation trace — each step can show which source influenced an output
• Sandbox execution — steps run in isolated sandboxes with controlled network access
• Replay & forking — any run can be forked at any step and replayed with different parameters

That combination lifts multi-agent setups out of the "takes a week to debug" bucket.

A pipeline we currently run

For the publisher, we built a research pipeline that runs weekly and gathers new sources on a defined topic area. Simplified:

┌────────────────────────────────────────────────────┐
│  Trigger (cron, webhook or Slack command)          │
└─────────────────────┬──────────────────────────────┘
                      ▼
        ┌─────────────────────────┐
        │  1. Scope agent         │  → defines: what exactly are we searching for?
        └────────────┬────────────┘
                     ▼
        ┌─────────────────────────┐
        │  2. Discovery agents    │  → 4-8 parallel web searches
        └────────────┬────────────┘
                     ▼
        ┌─────────────────────────┐
        │  3. Dedupe & rank       │  → MCP call against existing data
        └────────────┬────────────┘
                     ▼
        ┌─────────────────────────┐
        │  4. Extraction agents   │  → structured fields per source
        └────────────┬────────────┘
                     ▼
        ┌─────────────────────────┐
        │  5. Reviewer agent      │  → quality filter, citation check
        └────────────┬────────────┘
                     ▼
        ┌─────────────────────────┐
        │  6. Sink                │  → MCP call into editorial CMS
        └─────────────────────────┘

Six steps, two parallelized. Runtime per week: ~8 minutes, cost: well under what a working student would have cost for the same work — with better citation discipline.

Architecture choices that paid off

1. Skills instead of one giant system prompt

We define each agent as a small skill with a clear job, a clear input/output schema, and its own tool list. That makes debugging and replay much easier because each step is testable in isolation.

2. MCP as the only outer edge

Pipelines talk to the outside world exclusively through MCP servers. Database access, CMS updates, source repositories — all MCP. That gives us:

• A unified auth model
• Unified audit logs
• Swappable backends without pipeline changes

If you don't have a production MCP server yet, this is a good time — see our retrieval layer article.

3. Reviewer agent is mandatory

The second-to-last step of every pipeline is a reviewer agent. Its job isn't "make it better" — it's to reject when citation trace is missing, fields are empty, or a source is flagged unreliable. That alone cut "weird" production outputs dramatically.

4. Replay-ability from day one

Every pipeline run gets an ID, every step is logged with input/output. If step 4 fails, we can replay steps 1-3 without re-executing them. That cuts debugging time roughly in half.

Cost model — honest numbers

For the research pipeline above:

That's a weekly run for a full editorial research cycle. Even with a 4-5x safety margin we're under €5/month. This is the order of magnitude where multi-agent setups become economically viable for mid-market.

Where Antigravity (still) doesn't fit

• Real-time answers under 2 seconds. Pipelines have orchestration overhead. For chat latency, use direct LLM calls.
• Highly regulated data flows. Cloud sandbox execution means cloud. If data can't leave, look at Local AI.
• Pipelines with fewer than 3 steps. Antigravity overhead outweighs the benefit. A direct tool-use call is enough.

Anti-patterns we've seen

• "One agent per micro-task." Pipelines with 20+ steps become unmaintainable. We stay below 8.
• No output schema. When agents return free text, the next step breaks. JSON schemas are mandatory.
• Pipelines without a reviewer. A final quality gate is not optional.
• Models picked by gut feel. We benchmark every step with 2-3 models before committing. The cost table above came from two days of measurement.

How we introduce Antigravity in client projects

Our default path:

1. Workshop: identify a use case that runs manually today and has well-defined input/output
2. Prototype (2-3 days): stand up the pipeline without production hardening
3. Eval setup: 20-50 test inputs that every pipeline version runs against
4. MCP integration for the most important backends
5. Reviewer layer and replay logging
6. Soft launch with human approval before the sink step
7. Auto-run after 2-3 weeks of stable soft-launch behavior

Not spectacular, but it's the path that has reliably produced pipelines clients actually trust.

Bottom line

In 2026, Antigravity is the first multi-agent stack we're willing to take into mid-market client projects with a clear conscience. The combination of MCP-nativeness, sandbox execution and replay-ability moves the conversation past "interesting lab demo" into daily production use.

If you're considering multi-agent seriously, two prerequisites matter: a production MCP layer and a clear sense of which manual workflow should go first. With those in place, the pipeline is the fast part.

Have a workflow that screams multi-agent pipeline? Let's talk →