AI-Native Engineering

The pain

You probably landed on this page because you already have AI agents in production and one of these sounds familiar:

• You launched the first AI agent 6 months ago. It works well in the demo but degrades silently in production. When a user reports “the agent answered badly”, you can’t reproduce the case because you didn’t save the trace or the retrieval context.
• The product team asks for “agent metrics”. The engineering team delivers a dashboard with call count, p99 latency and tokens consumed. None of those metrics measure whether the agent is answering well. The CEO sees green, users see inconsistent answers.
• There’s an evaluation pipeline but it runs only on the initial golden dataset, measures accuracy against static ground truth, and nobody knows if the results are statistically significant or noise. Every time a prompt changes, the evaluation suite “goes up” or “goes down” without anyone being able to interpret the difference.
• The agent makes decisions affecting money, health or critical operations. When it fails, there’s no runbook. The on-call gets an alert without context and has to manually reconstruct what the agent did, what documents it retrieved, what context it had.
• The team knows it needs “agentic SRE” — observability, alerting, continuous evaluation, model rotation, A/B testing with statistical rigor — but is building prompts while the agent is in production, with no capacity to stop everything and build operational discipline from scratch.

None of these is a model problem. All are production engineering problems applied to AI agents, a field where most teams are learning live with real money.

What CultureTech prepares

A Staff Engineer embedded with the client team for 3-6 months, focused on one of these domains (not all five at once):

• Agentic SRE — observability, SLO-driven alerting, postmortems, operational runbooks designed for AI agents, not deterministic microservices. SLOs based on quality metrics, not just latency. Burn rate alerting that accounts for an agent giving bad answers for minutes before the business notices.
• Semantic LLM observability — instrumentation that captures the full context of each interaction: prompt, retrieved documents, intermediate reasoning, output, model version, embedding model version, retrieval strategy. Each response has reproducible full trace. Via Aether Telemetry, integrated into the existing observability stack, not as a separate tool.
• Evaluation engineering with rigor — dynamic datasets that grow with production feedback, A/B testing with stated and applied statistical significance, automatic regression detection when a prompt or model changes. It’s not “raise accuracy” — it’s knowing with how much confidence it’s rising.
• Operational agent governance — who can deploy a prompt change to production, how a model is rotated, how to roll back when the agent starts degrading, when fine-tuning is safe vs when changing the system prompt is enough. Compliance-aware if the sector requires it.
• Agent cost engineering — real attribution of cost per query, per feature, per user. Informed decisions on when caching is worth it, when a smaller model is worth it, when fine-tune vs prompt engineering is worth it.

The engagement delivers code in your repo, dashboards in your observability stack, evaluation suites in your CI, runbooks your on-call knows how to execute. No “AI strategy deck”, no “adoption roadmap.”

Who it’s for

• Organizations with AI agents already in production (not pilot) feeling they’re losing operational control. If your agent isn’t in prod yet, this service is premature — get to prod first, then call us.
• Sectors where the cost of agent error is high: banking (decisions affecting cards, credit, fraud), healthcare (diagnostic assistance, triage), telco (operational recommendations, billing).
• Engineering teams that already have mature SRE/Platform for their deterministic systems but recognize AI agents need a different operational discipline they haven’t developed yet.

What it’s not

• Prompt engineering consulting. We don’t sell prompts. We sell the operational discipline to evaluate, deploy and operate prompts as versioned artifacts.
• Generic RAG implementation. If you need a RAG pipeline from scratch, there are specific vendors. This practice serves what comes after: making that RAG operationally reliable.
• Fine-tuning as a service. Not our focus. If fine-tuning is part of the solution, we help you operate the cycle (when, how often, rollback) but we don’t train the model.
• AI strategy. If you need to decide whether your organization should use AI, that decision is yours. This practice starts after that decision, when there’s already an agent that must operate well.

How it crosses the portfolio

• Aether Telemetry — the semantic observability layer capturing full context of each agent interaction. Enables reproducing bugs, attributing costs, and generating evaluation datasets from real production traffic.
• Themis — AIOps agents consuming Aether signals to automate runbooks. Applies when the client wants the agent-of-agents pattern (agents operating other agents) with operational discipline from day one.
• Agentic SRE — the playbook introducing AI agents into the SRE cycle without losing operational rigor.
• AI Governance LATAM — the 4 dimensions (identity, evaluation, observability, accountability) to govern agents in production with regional compliance.

When it activates

Q3 2026. Before that, the senior capacity with the specific combination (LLM ops + SRE + statistical rigor) is committed to the initial portfolio launch.

The process: discovery week (no cost, requires access to current observability and evaluation) → proposal with scope limited to one domain → 3-6 month engagement → documented handoff. No mandatory post-engagement retainer.