Building Production Infrastructure for an AI Agent Engineering Platform

An AI infrastructure startup building an Agent Engineering Platform — a control layer for designing, evaluating, observing, and optimizing AI agents in production.

The platform brings software-engineering discipline to AI agent development by providing infrastructure for evaluation, orchestration, reliability, gating, and continuous optimization based on real production usage data. Engineering and product teams use it to ship AI agents from lab to production at scale — with measurable accuracy, cost, latency, and safety guarantees.

The client is under NDA; details in this case study are intentionally anonymized.

Teams shipping AI agents face an exploding configuration space — combinations of prompts, models, retrieval strategies, tools, and guardrails — where roughly 95% of configurations are suboptimal. Manual tuning drains engineering cycles, quality and safety risks slip into production, and there is no credible way to back configuration choices with evaluation data.

Our client's platform addresses this directly with evaluation pipelines, optimization workflows, and quality gates that move AI agent development out of prompt guesswork and into systematic engineering. To support that mission, the platform itself needed production-grade backend infrastructure: reliable distributed pipelines, stable SDK-friendly APIs, multi-tenant isolation, and deep observability across cost, latency, and safety budgets.

The client engaged CodeTiburon at an early stage to bring senior platform engineering capacity to the team — owning Python microservices, distributed workflows, and developer-facing tooling end-to-end.

CodeTiburon embedded a senior Python platform engineer (10+ years of production systems experience) into the client's small founding team. The engagement focuses on owning backend services and distributed workflows end-to-end, while contributing to developer tooling and the platform UI where needed.

Scope of work

• Backend ownership. Evolving Python microservices and async, event-driven workflows.
• Distributed evaluation pipelines. Hardening execution, scheduling, retries, idempotency, and fault tolerance.
• API design. Stable, versioned APIs with SDK-first ergonomics and strict backward compatibility.
• Production reliability. Multi-tenant SLAs, queueing and backpressure, retries, and isolation.
• Engineering quality. Unit, integration, and contract tests; CI/CD gates; release discipline; documentation.
• Observability. Metrics, logging, distributed tracing, profiling, and unified cost/latency budgeting.
• Static analysis & instrumentation. ASTs, linters, rules engines, and program transformation for the platform's developer-facing code intelligence layer.
• Light full-stack contribution. Platform UI features in React and TypeScript.

The work operates within the client's developer platform — a domain-specific specification language and SDK that let developers define tunable decisions across models, prompts, tools, retrieval strategies, and constraints, and gate changes in CI against accuracy, cost, latency, and safety KPIs.

The engagement is ongoing, and the work is steadily moving the platform from early-stage product toward a production-grade AI agent infrastructure. Tangible contributions to date:

• More reliable distributed workflows through hardened scheduling, retries, and idempotency in evaluation pipelines.
• Stable, backward-compatible APIs that let SDK and CLI consumers evolve without breakage.
• Production-grade multi-tenant backends with clearer SLA, queueing, and backpressure behavior.
• Deeper observability across metrics, logs, tracing, profiling, and cost/latency budgeting.
• Higher engineering quality via testing strategy, CI/CD gates, and release discipline.

The collaboration supports the client's goal that defines its category: turning AI agent development from prompt guesswork into a systematic, measurable, production-ready engineering discipline.