Glossary
Agentic AI,
defined plainly.
Clear definitions of the terms behind agentic AI systems — what they mean, and what they look like in production.
What is an agentic AI system?
An agentic AI system is software where AI agents take actions and make decisions on their own — planning, executing, and adapting — instead of only answering questions or following fixed rules.
Unlike a chatbot that responds to prompts or an automation that runs a script someone wrote, an agentic system pursues a goal: it decides which steps to take, calls the systems it needs, checks its own work, and escalates to a human only when something genuinely requires judgment. Retter designs, builds, and runs these systems on AWS serverless for enterprise operations, commerce, loyalty, and reporting.
What is an agentic backend?
An agentic backend is an operational core where AI agents run the work — routing orders, reconciling data between systems, and fixing failed processes — instead of a team babysitting scheduled jobs and brittle integrations.
A traditional backend executes instructions and pages a human when something breaks at 2 a.m. An agentic backend acts: agents handle repetitive tasks without human clicks, catch and fix issues before they become outages, and stay inside limits you set. Retter builds these new or layers them onto systems you already run — a first workflow is usually live in 6–8 weeks.
What is agentic e-commerce?
Agentic e-commerce is a store where AI agents do the selling, support, and fulfillment — guiding each shopper, answering questions any hour, and orchestrating orders — rather than leaving the customer to do all the work.
An agent understands "something for a dinner party of eight" and assembles the basket; for marketplaces, agents own seller onboarding and product approval end to end. Retter builds this on a platform serving 20M+ consumers — or on top of the store you already run, live in as little as 2 weeks.
What is agentic loyalty?
Agentic loyalty is a loyalty program run by AI agents that choose the offer, timing, and channel for each member individually — and design, test, and improve campaigns themselves — instead of blasting one promotion to everyone.
Where marketing automation runs the campaign someone designed, an agentic program designs it: it watches each member’s behavior, runs the experiment, and keeps what works, inside margin and frequency limits you set. Retter runs loyalty this way for Starbucks across 3 countries and modernized CRM, loyalty, and wallet for Emirates Leisure Retail across 300+ outlets.
What is agentic reporting?
Agentic reporting is analytics you query in plain language: an AI agent with governed access to your data plans the analysis, runs the queries, checks its numbers against known totals, and answers — no dashboards or SQL.
Dashboards answer the questions someone predicted last quarter; an agent answers the one you have now, like "why did basket size drop in Riyadh last weekend?", and shows the queries it ran so analysts can verify instead of reconstruct. Because the data already exists, Retter’s reporting agents are usually live in 3–4 weeks.
What is an AI-native platform (vs. AI features)?
An AI-native platform is built around one shared stream of customer events that every capability reads from, so intelligence is the foundation — not a chatbot or recommendation widget bolted onto a legacy stack.
When search, recommendations, loyalty, and pricing all read the same event substrate, search understands intent from purchase history and loyalty triggers on real behavior; bolted-on AI features leak context at every seam between systems. AI-native does not mean starting over — Retter’s enhance engagements add the intelligence layer on top of platforms you already run.
What is AWS serverless, and why does Retter build on it?
AWS serverless is cloud infrastructure (Lambda, DynamoDB, API Gateway, S3) that scales itself on demand and bills for actual usage, so there are no servers to provision, patch, or pay for while idle.
A loyalty push to a few million members generates a wall of traffic in seconds; serverless absorbs it without a capacity meeting, then costs nothing when traffic falls to zero at 4 a.m. Retter has run enterprise platforms on this foundation for a decade, with typical cloud-cost reductions around 70% and zero downtime in production.
What is the Retter Orchestrator?
The Retter Orchestrator is the system that takes enterprise AI agents to production — secure, governed, and live in weeks — with scoped permissions, budgets, approval gates, and a full audit trail.
Almost anyone can build an AI agent demo; the hard part is running agents reliably enough for a real business. The Orchestrator is what makes that safe: every agent works inside limits you set, and every decision is logged. It runs on top of Rio, Retter’s serverless platform.
What is Rio?
Rio is Retter’s serverless platform on AWS — the decade-old foundation that carries national-scale grocery, omnichannel retail, and multi-country loyalty, scaling through campaign spikes and billing for use, not idle servers.
Rio is what the AI agents run on top of. It powers platforms like Gratis’ omnichannel commerce and MMI’s multi-brand backend, and it is the reason the reliability underneath Retter’s new agentic layer is a decade old even though the intelligence is new.
What are Retter’s two engagement models?
Retter works two ways: build a new AI-native platform end to end, or enhance the platforms you already run by layering agentic outcomes on top — no replatforming required.
The build model delivers the whole thing — commerce, loyalty, backend, and the agents that operate them — production-grade from day one. The enhance model connects agents to your existing ERP, commerce stack, or loyalty system through APIs, so your systems stay in place and intelligence goes live on top of them in weeks.
How are enterprise AI agents kept safe in production?
Guardrails are the limits an AI agent operates inside: scoped permissions on what it can access, budget and rate caps, human approval gates for irreversible actions, and a full audit trail of every decision.
This is the difference between a demo and an enterprise system. With guardrails, an agent can run real operations — spending, messaging, order changes — because you control exactly what it may do and can review everything it did. Retter builds these controls into every engagement.
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