Frequently asked questions

Classic taxonomy: simple reflex, model-based reflex, goal-based, utility-based, and learning agents. In production the distinction that actually matters is narrower — how much autonomy the agent has over real actions, and what guardrails constrain it.

Automate the repetitive, keep humans on the complex. Identify recurring intents, handle them with deterministic flows, and call the model only at genuinely ambiguous points. On Cardynal this handles roughly 1,300 conversations a day and cuts tickets by about 80%.

A demo takes days; a production agent takes the reliability layer underneath — validation on every tool call, deterministic fallbacks, observability, and a defined escalation path. Most of the engineering is that layer, not the prompt.

A deterministic system returns the same output for the same input — testable, auditable. An agentic system lets the model decide what happens next: flexible, but with no guarantee. Sound architecture uses both: deterministic where correctness is non-negotiable, agentic where the problem is genuinely fuzzy.

A flow whose steps and transitions are defined in advance — rules, a state machine — rather than chosen by a model at runtime. You can test it, replay it, and prove what it did. It's the spine you own, as opposed to the engine you rent.

Language models are stochastic: identical input can yield different output. That's exactly why critical logic — pricing, business rules, anything contractual — belongs in tested code, with the model invoked only at fuzzy decision points.

SEO optimizes for ranking in a list of links. AEO — answer engine optimization — optimizes for being the answer: a direct response at the top of a section, structured data declaring what you are, and facts precise enough to quote without rewriting.

No — it layers on top. The technical foundation is identical: crawlability, speed, correct hreflang. What GEO adds is answer-first structure, declared entities, and content specific enough that a generative engine can cite it rather than paraphrase a competitor.

Be extractable and attributable. Lead each section with the answer, declare your entities in structured data, keep facts verifiable and dated, and let AI crawlers in via robots.txt. Content that buries its answer inside an argument doesn't get quoted.

No. AI speeds up variant production and journey analysis, but conversion optimization remains a discipline of hypotheses and measurement. What changes is testing throughput — not the need to decide what to test and to read results honestly.

Building the smallest working product that tests whether an AI feature actually creates value — with real users and real data. Not a prototype: a restricted but functional product, scoped to answer one question fast.

You can get to a working demo quickly. The gap to production is architecture, edge cases, security, deployment and maintenance — plus the reliability layer that keeps an AI feature from failing silently. That gap is where most projects stall.

Because the cost isn't in the visible features. It's in scoping, integrations, edge cases, and the reliability work that makes it survive real users. A cheap MVP usually means one of those was skipped — and it resurfaces later at a higher price.

Start from what's measurable: Core Web Vitals green on mobile, real accessibility, SEO and GEO foundations designed in rather than bolted on, and code you own. Visual craft matters, but it's what sits on top of those four — not a substitute for them.

Someone who works across the whole path — product decisions, system architecture, and shipping to production — rather than only training models or only writing frontend. In practice: deciding what to build, designing the reliability layer, and running it live.

You talk to the person who builds. No account manager in between, no junior assigned after signing. The honest trade-off is limited capacity — few projects run in parallel.