Replit's marketing agent: 14,230 lines, $254/month, on Haiku

This is the most complete production-agent build sheet I've seen anyone publish, and almost every number in it argues against how the rest of us are building agents.

Replit disclosed the internals of two production agents at SaaStr AI 2026: 10K, an autonomous VP of Marketing, and QBee, an autonomous VP of Customer Success running 100+ sponsors on a daily QBR cadence at 70% fewer human hours than a comparable B2B media operation. (SaaStr)

10K is 14,230 lines of code. It costs about $254/month incrementally. It runs largely on Claude Haiku and Mini models, not frontier. It sent 331 investor outreach emails with zero send failures, drove $1M+ in closed agent-sourced pipeline, and hit 72% open rates on win-back campaigns. Three humans and 20+ agents run the whole operation.

Fourteen thousand lines. That's the number I want people to sit with. The dominant mental model of an agent is a good prompt plus some tools, and that model produces demos. A production agent that replaces a function is a program, and most of that program is scaffolding: context compaction, long-term markdown memory files (replit.md), a monorepo giving agents global business context, and a self-improving loop where the agent reviews nightly traces, opens prompt-change PRs, and A/B-tests them itself.

That last piece is the one I'd steal tomorrow. Nightly trace review that emits a PR is a self-improvement loop with a human gate and a version history. Compare it to story #4, where unconstrained self-revision destroys work.

The model tier matters just as much. Haiku and Mini, not Opus. When your agent is 14,230 lines of deterministic scaffolding wrapped around narrow model calls, you don't need frontier reasoning on every call, and $254/month is what that architecture costs. The frontier-model-for-everything approach is a symptom of thin scaffolding.

Vercel published a matching build sheet (SaaStr): three people (GTM engineer, data scientist, subject-matter expert) shadowed the top SDR for days converting workflows into tool-calling steps, then six weeks in shadow mode with human review before full autonomy. Infrastructure and tokens run ~$5,000/year plus 20% of one engineer, against ten replaced salaries. The agent performs like a "90th-percentile rep 100% of the time." Their support agent handles 93% of a technical caseload; their content agent completed 96% of major content updates last quarter.

The line in Vercel's account that matters most: the replacement pattern required rebuilding the workflow around the agent, not bolting an agent onto the existing one. Six weeks of shadow mode is the discipline nobody blogs about.

Pricing is converging on the same shape. HubSpot's Customer Agent is $0.50 per resolved conversation, Intercom Fin $0.99, Zendesk ~$1.50 (SaaStr). SAP's Christian Klein told analysts SAP wants to "completely reset the price level" toward outcome-based pricing, with 400+ Autonomous Suite agents planned by year-end (ERP Today).

If you're quoting agent work, the comparison isn't a seat license anymore. It's cost per unit of work, and Replit just published theirs.