API & Backend Engineering
Every agent, every AI feature, every automation eventually cashes out into backend fundamentals: an API contract someone has to design well, a data model that either fits the domain or fights it forever, an auth system that's either airtight or a breach notification in waiting. The AI era didn't retire backend craft — it raised the load on it, then pointed untrusted model outputs at it.
This is the substrate skill under everything else we do. APIs designed as contracts with versioning and pagination thought through before v1 ships; authorization enforced server-side with the rigor agent access demands; integration architecture for the SaaS sprawl and legacy systems agents need to reach; and the performance discipline — profiling, caching, queueing — that keeps p99s flat when traffic isn't. Senior backend engineering, applied where the stakes went up.
API design and build
REST and gRPC contracts with versioning, pagination, and idempotency designed in — not bolted on.
Authentication and authorization
OAuth, RBAC, and tenant isolation enforced where it counts — including for agent callers.
Data modeling
Schemas that fit the domain and survive its evolution — the decision that outlives every refactor.
Integration architecture
Legacy systems, SaaS APIs, and webhooks unified behind clean, governed interfaces.
Load and reliability engineering
Caching, queueing, backpressure, and the profiling habit that keeps latency flat at 10x traffic.
What changes about API design when agents are the callers?
Agents amplify every design flaw: ambiguous parameters produce wrong calls at machine speed, missing idempotency turns retries into duplicate side effects, and weak authorization becomes an open door probed tirelessly. The fixes are classical API discipline — applied with the assumption that your next high-volume client doesn't read documentation, it samples from it.
Monolith or microservices?
A well-modularized monolith until you have a demonstrated reason — independent scaling, genuine team boundaries — to pay the distributed-systems tax. Most microservice pain we're hired to fix is a premature decomposition. Architecture should follow measured need, not conference fashion.
Can you work inside our existing codebase rather than greenfield?
That's the default. Forward deployment means your repo, your conventions, your CI — improved incrementally from within. We leave codebases better than we found them and, more importantly, leave your team able to maintain what we built.
Put API & Backend Engineering to work — in production.
One forward-deployed engineer, embedded in your stack, owning the outcome from discovery to production. Weeks, not quarters.
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