We build AI agents that survive the next release cycle — not pilots that demo well and stall at production. Every agent we ship has a named business owner, an automated eval suite that gates deployments, and an observability layer that surfaces drift before customers do. If that sounds like the bar for ordinary software, that’s the point.

What we build

Replyant’s engineering practice covers the full surface area of production agent work. The discipline is narrow on purpose — we don’t sell models, we don’t sell platforms, we sell the engineering that turns a model into a system your operators can defend.

  • Custom single agents. Support, research, lead qualification, internal tools, document processing. Built around your actual workflow — not a template — with the same rigor Anthropic uses on Claude Code’s own system prompt design.
  • Multi-agent systems. When one agent can’t hold the context or the workflow needs genuinely different reasoning strategies at different stages, we design orchestration patterns that compound value instead of compounding token spend. The architectural tradeoffs are covered in our multi-agent systems lab post.
  • MCP integrations. The Model Context Protocol is collapsing the integration tax — one MCP server per system, any MCP-compatible agent can use it. We build production MCP servers for your internal systems, with the auth, rate limits, and error handling that demo servers skip. See our breakdown of how MCP actually works under the hood.
  • Evaluation pipelines. No agent ships without an automated eval suite running in CI. Task completion, tool selection accuracy, cost per task, and safety compliance — gated on every deployment. The full architecture is documented in our agent evals in CI/CD post.
  • Production hardening. Logging, kill switches, drift monitoring, retry budgets, escalation paths. The unglamorous engineering that determines whether the agent is still working three months after launch.

The published price range for custom agent work runs from roughly $35,000 for a focused single-workflow build to $200,000+ for multi-agent systems with deep enterprise integration. Most engagements land somewhere in the middle. The cost driver isn’t the model — it’s the integration depth and the eval coverage.

Who it’s for

This service is built for operators who need agents running in production, not slide decks.

  • You have a workflow that’s already costing you measurable money — support volume, qualification backlog, document processing, internal request triage — and the math for automation works on a Year 1 or Year 2 horizon.
  • You have data infrastructure that’s at least navigable — or you’re willing to fix it as part of the engagement. Agents fail on bad data faster than humans do.
  • You can name a business owner before the engagement starts. Not “IT will handle it.” A specific person in the business unit who is accountable for the agent’s outcomes and has time to participate in design.
  • You can define success in numbers — resolution rate, cost per ticket, qualification velocity, error rate. “Better customer experience” is not a target we’ll build against.

If any of the four are missing, we’ll tell you in the discovery conversation and recommend strategic consulting or business automation instead. Building the wrong agent on top of the wrong foundation is the most expensive mistake in this category.

How we engage

Four phases, no surprises. Six to twelve weeks end-to-end for a typical single-agent build; longer for multi-agent systems with heavy enterprise integration.

  1. Discovery (1-2 weeks). Process audit, data audit, integration map, eval targets, success metrics. We produce a written design document and a fixed-price build estimate. If we surface a reason not to build, we’ll say so before you commit to phase two.
  2. Build (4-8 weeks). Iterative delivery against the design doc. System prompt engineering, tool definitions, MCP server work, integration plumbing, eval suite, observability hooks. Weekly working demos against the eval suite — not vanity demos.
  3. Ship (1-2 weeks). Shadow-mode deployment in your production environment. The agent runs alongside the existing process, decisions are logged but not executed, the eval suite validates against real traffic. Cutover only when the numbers clear the threshold you set in discovery.
  4. Operate (ongoing, month-to-month). Drift monitoring, eval regression triage, model upgrades, prompt tuning, new tool additions. Most engagements include a retainer for the first 90 days; some clients keep us on indefinitely as the agent surface area grows.

We don’t do fixed-scope multi-quarter engagements. The work is too sensitive to model and protocol changes to commit to a static deliverable nine months out.

Our engineering work is grounded in the same architecture we write about publicly. If you want to evaluate how we think before you talk to us, these are the most relevant pieces: