cashcrown // AI agents

Autonomous agents and AI orchestration

Agents that do the work. Not ones you chat with.

Most "AI in business" ends with a chat that needs babysitting. An executive agent is different: it gets a clear scope, tools, and safety gates, and access to models always goes through a router — with PII masking before the cloud.

We build agents for customer service, sales and SDR, emails, documents (OCR, classification), quoting, and system integrations. Orchestration via state graphs (LangGraph, MCP) provides control over workflows and auditability at every step. We start with a single process and a pilot — we show a working agent before asking for trust.

// 01Problem

Most „AI in the company” ends with a chat you have to babysit. Processes still need a human in every loop — lead qualification, email handling, quoting, documents. Time drains into repetition, not decisions.

// 02Approach

We build agents as an execution layer: plan → act → verify, with access to tools (APIs, databases, queues) and a hard log. Every agent has a clear scope, safety gates and reports the truth — not optimism. Orchestration runs through state graphs; model access always goes through the router.

AI gateway / routerLangGraphMCPn8nFastAPIkolejki zadań

// 03Process

Process map
We break the real flow down into steps, data and decision points.
Agent + tools
We define the scope, tools and gates; we wire in the LLM router.
Loop with proof
Plan → execution → verification (log/test), with rollback.
Deployment and oversight
Observability, alerts, a gradual widening of autonomy.

// 04What you can build

cashcrown@lab: agenci --listready

customer support agent — answers 24/7, escalates, logs
sales / SDR agent — qualifies leads and follows up
email agent — sorts, drafts, summarizes
quoting automation — estimates and offers from data
document agent — extraction, classification, OCR

// 05Examples: how we build it

Ready systems in this area — from measured models and components, to try live:

Concierge RAG — an assistant on your knowledgeWe turn a customer question into a cited answer — from your knowledge, 24/7, no hallucinations.An autonomous agent with toolsAn agent that does tasks with tools — under supervision (a human gate on irreversible actions).Doc Intelligence — a document into queryable knowledgeAn uploaded PDF/image becomes a summary, extracted fields and Q&A with citations.Multilingual help desk — one team, every languageThe customer writes in their language, the agent reads in theirs — translation, RAG and routing in one flow.Research agent — from sources to a reportGathers public sources, verifies, synthesizes and writes a cited report — under supervision.

// 06FAQ

Does the agent have access to our systems?

Only within the scope you set — through API contracts and permission gates. Every action is logged and reversible.

What about data security?

Model access goes through the router; we mask PII before anything leaves for the cloud, and we handle sensitive paths locally (self-hosted LLM + BGE-M3).

How do we start?

With an audit of a single process and a pilot. We show a working agent before we ask for your trust.

How much does an agent cost and when does it pay off?

We work in ranges that depend on scope — the entry point is a fixed-cost pilot with one measurable KPI. If a process eats a dozen to several dozen hours a month, the deployment usually pays back in 2–4 months. Calculate the return in our ROI calculator.

Is the agent compliant with the AI Act and GDPR?

Yes — we design compliance in from the start: the agent introduces itself as AI (transparency), irreversible actions pass through a confirmation (human-gate, human oversight), and every step is logged. We mask PII before the cloud; profiling or decisions about people add a DPIA.

// →Related

Services in this domain

Product: OpenClaw

Case studies

Let's start with an audit and a pilot.

We show a working system before we ask for your trust.

Book a call

Autonomous agents and AI orchestration

Agents that do the work. Not ones you chat with.

// 01Problem

// 02Approach

AI gateway / routerLangGraphMCPn8nFastAPIkolejki zadań

// 03Process

Process map
We break the real flow down into steps, data and decision points.
Agent + tools
We define the scope, tools and gates; we wire in the LLM router.
Loop with proof
Plan → execution → verification (log/test), with rollback.
Deployment and oversight
Observability, alerts, a gradual widening of autonomy.

// 04What you can build

cashcrown@lab: agenci --listready

customer support agent — answers 24/7, escalates, logs
sales / SDR agent — qualifies leads and follows up
email agent — sorts, drafts, summarizes
quoting automation — estimates and offers from data
document agent — extraction, classification, OCR

// 06FAQ

Does the agent have access to our systems?

Only within the scope you set — through API contracts and permission gates. Every action is logged and reversible.

What about data security?

Model access goes through the router; we mask PII before anything leaves for the cloud, and we handle sensitive paths locally (self-hosted LLM + BGE-M3).

How do we start?

With an audit of a single process and a pilot. We show a working agent before we ask for your trust.

How much does an agent cost and when does it pay off?

Is the agent compliant with the AI Act and GDPR?

Let's start with an audit and a pilot.

We show a working system before we ask for your trust.

Book a call