Project
$15k+fixed scope
Fixed-scope build with a senior engineer leading. The most common engagement.
- Senior engineer on code
- Fixed scope + quote
- 4–8 weeks to ship
- 30-day support
Hire AI Agent Developer
Hire an AI agent developer who’s actually shipped agents.
Agents that work in production look nothing like the demos. We've built and shipped the production version — Arlo runs in production for agencies today. You get that experience on your project.
The problem
Most AI agent developers have built one chat-with-PDF demo.
The shape of agent work has changed dramatically in 18 months. Tool calls, MCP, memory, evals, observability, human-in-the-loop, model routing — getting any one of these wrong sinks the project.
Most “AI agent developers” you can hire have built a thing on a weekend and don’t have the production scars. They’ll ship you a demo. It won’t survive contact with real users.
What we do
We’ve shipped agents that actual users use.
Arlo, our own product, is an MCP-powered AI agent connecting Claude to 100+ analytics platforms. It’s used in production by agencies managing dozens of clients. Pass-through architecture, zero data retention, encrypted tokens.
Same patterns apply to your customer-support agent, your sales agent, your internal-ops agent. We’ve done the production work — that’s the experience you get.
Capabilities
Agent architecture
Single-agent or multi-agent, tool inventory, memory strategy, escalation rules, eval criteria — all designed before any code.
MCP tool integration
Wire agents into your real systems via Model Context Protocol. CRM, helpdesk, database, calendar, payments.
Persistent memory
Conversation memory across sessions, user-specific context, long-term fact memory. Tunable retention and redaction.
Structured outputs + validation
JSON schemas, Zod validation, type-safe outputs into downstream code. No 'creative' agent outputs into critical paths.
Human-in-the-loop
Confidence-based escalation. When the agent isn't sure, it hands to a human with full context. No black-box hallucinations.
RAG over your data
Retrieval-augmented generation against your docs, knowledge base, support history. Answers from your information.
Evals + testing
Eval suites against real-world cases. Quality measured. Regressions caught before they reach production.
Observability
Full traces of every agent run. What was asked, what tools were called, what the model thought, why it escalated.
Cost + latency control
Right model per step (Haiku/Sonnet/Opus). Caching, batching, budget alerts. Production agents that don't run wild on model spend.
How we work
How an engagement starts.
- 01
Discovery call
30 minutes. What you're trying to ship, the constraints, the timeline.
- 02
Written proposal
We come back with scope, fixed quote, and timeline. No deck.
- 03
Kickoff
Week 1 we're embedded. Slack, weekly cadence, continuous deployment.
- 04
Ship
Working build in 4–8 weeks for most engagements.
- 05
Run
Optional retainer after launch. Same team. Same Slack channel.
Engagement
Engagement options.
We don't sell hours. We sell shipped work. The two shapes we offer:
Embedded
$12k/mo16 hrs/week
Embedded part-time in your team. For ongoing work or longer roadmaps.
- Senior engineer 16 hrs/week
- Same person every week
- Slack access
- Month-to-month after 90 days
Fractional CTO
$12k+/mo
If you need senior tech leadership across the whole engineering function — not just one role.
- Strategy + hands-on
- Hiring + leadership
- Architecture decisions
- Board support
If a marketplace developer at $80/hour fits your need better than us, we'll honestly tell you.
FAQ
Overlap is significant, but agents have specific complexity: tool orchestration, memory, escalation, observability. We do both, but if your project is specifically an agent, this is the right page.
Yes — all of them. We're not loyal to one framework. MCP is the emerging standard for tool integration. LangChain/LangGraph are useful for orchestration. We pick per project.
Yes — we do this regularly. A brief audit first to see if the existing code is salvageable or needs a rewrite. We won't pretend the existing thing is good if it isn't.
Default to Claude for reasoning and tool use. OpenAI when the task demands it. Open models for cost/compliance. Model choice is per use case, not loyalty.
Structured outputs, validation, human-in-the-loop escalation, eval suites, observability. Most of the actual work is making the agent fail safely on the cases it can't handle.
Optionally, yes. Monthly retainer for ongoing AI ops. Most clients keep us on after the initial build.
Want to talk about your project?
Tell us what you're building or trying to figure out. We'll come back with what we'd do, how long it takes, and what it costs. No deck, no sales call.