Do more with the team you have.
Agentic development multiplies what your team could ship, but the systems behind the code have to scale with the output. When coding speeds up by an order of magnitude, the challenge shifts to everything that supports it: quality and security review, cost control, infrastructure, observability, and operations.
AI Platform Engineering is the evolving state of the art for agentic systems management: token optimization and cost attribution, runtime guardrails, durable execution. We bring over 25 years of experience designing production infrastructure at Amazon, Bazaarvoice, and OJO Labs, across three IPOs and eight acquisitions, to help your team scale successfully.
Three things become yours the moment agents go live
Live agents and agentic development workflows cost money on every call, can reach sensitive systems and data when things go wrong, and have to add value in the long run.
Financial Operations
Token and inference cost optimization per task, per agent, per tier. Budget caps, semantic caching, and pre-emptive monitoring that catches a runaway loop before it shows up on the bill.
Risk Management
Runtime action management (allow, deny, defer), sandboxing, least-privilege agent identity, and human-in-the-loop gates expressed as policy.
Platform Reliability
Durable execution with checkpointed resume, progressive delivery for stateful agents, and eval harnesses wired in as regression gates.
How we work
We start every engagement with one question: where are your bottlenecks and risks, and where can we automate? Answering it takes an honest look at your data, workflows, and people, and the recommendation reflects what's right for your business, including the timing.
This matters because the operational layer is where AI work succeeds or fails. Anthropic's research shows effectiveness varies sharply by expertise, part of why most AI initiatives underdeliver (NTT DATA, 2024).
What we look at first
A written recommendation with clear reasoning, so the work that follows fits what your business needs.
What we test before we commit
Fast validation on prototype workflows to show whether the approach is sound before you commit to a full build. By the time you scale, you've seen it work on your data.
What you own at the end
What we build comes with documentation and training, runbooks for what's likely to break, and the source in your account, not ours. The handoff happens during the build, not at the end, so your team is part of the solution and experience.
Three ways to work together
Most teams start with an Audit. Some already know what they want built and skip ahead. A few want an ongoing partner instead of a project.
Start here if you're not sure
Audit
A grounded read on workflow efficiency; what your agents cost, where they can take an action they should not, and where they fail under load.
See details →Start here if you know what you want to build
Implementation
The operational layer your agents need: cost attribution, runtime guardrails, durable execution, delivered in phases you approve one at a time.
See details →Start here if you need an ongoing partner
Fractional Expertise
A senior engineer on call for your production platform: architecture review, cost and reliability guidance, without a full-time hire.
See details →The background behind the advice
We've built the systems that let engineering teams ship safely under pressure, through three IPOs and eight acquisitions, on platforms carrying Bazaarvoice and Amazon-scale traffic. Most people advising on AI have not run multi-billion dollar transaction platforms or guided thousands of services through major evolutions with zero downtime. We have. That is the background Agentic Development scale requires.
Not sure where to start?
That's the most common place to start. The first call is where we figure out whether it's the audit, the build, or someone other than us.