AI Agent Development

Your Team Stops Beingthe bottleneck.

Autonomous AI agents that receive a goal and complete it. Planning, deciding, and executing across your entire tool stack — around the clock, without a human managing each step.

Deployed in production
LangGraph certified builds
Response within 1 hour
Voice AgentHeliovia Medical
3/4 tasks
Live
Inbound call received and answered2 min ago
Appointment booked in calendar2 min ago
CRM record updated with call notes1 min ago
Confirmation SMS being sentnow
Outcome70% admin load reduced
3/4
Steps done
0:42
Duration
Running
Status
How It Works

What Autonomous Agents Actually Do

Four capabilities that separate a real AI agent from a workflow tool or a chatbot.

Goal inputClient request
OrchestratorTask planner
Shared memoryContext + state
Voice AgentInbound calls
RAG AgentDocument queries
Outreach AgentProspect research
Invoice AgentAP reconciliation
OutcomeAll agents complete
Orchestrator
Agents
Completed tasks
Memory
Capabilities

Six Ways We Remove a Human From a Process That Doesn't Need One.

Each capability below is a specialist build. Most engagements combine two or more.

Agentic Systems

Give it a goal. The agent plans, executes, and loops back — no human at each step.

High decision volumeMulti-system

RAG-Powered Intelligence

Agents reason over your documents, policies, and data in real time. No hallucinations.

ComplianceKnowledge-heavy ops

Multi-Agent Orchestration

Parallel specialist agents, one orchestrator. Complex workflows finished faster.

3+ systemsParallel execution

Voice AI Agents

Calls answered, appointments booked, CRM updated. Your team hears nothing — it just happens.

HealthcareInbound sales

Human-in-the-Loop

Full automation with precise handoff points. The agent handles the work. Humans approve what matters.

FinanceLegalCompliance

Custom Agent Builds

Your processes are not in any model's training data. We build agents specific to your logic and tools.

Proprietary opsSpecialist industry

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The Distinction

An Agent Is Not a Chatbot.

A chatbot responds to questions. An agent pursues goals. Here is what that difference looks like in practice.

Responds to a prompt
Pursues a multi-step goal autonomously
Needs a human for every action
Executes actions without being asked each time
Has no memory between sessions
Maintains shared memory across the entire workflow
Connects to one interface
Connects to every system in your stack
Stops at the edge of its window
Loops back, self-corrects, retries on failure
Produces text output
Produces outcomes: booked, filed, sent, reconciled

This is the difference between a tool and a team member.

Client Results

Real Outcomes. Measured in Production.

Every deployment below replaced a high-cost manual process with a system that now runs without the team's involvement.

Heliovia MedicalVoice AI Agent

The Problem

Inbound patient calls routed manually. After-hours calls missed entirely. Admin team overwhelmed with scheduling instead of patient care.

The Outcome

70%Admin load reduced

24/7 autonomous patient scheduling. Zero missed after-hours calls.

Retell AIPythonCRM Integration
MyMembaMulti-Agent System

The Problem

Multi-tenant CRM with no automated billing workflows. Manual Stripe reconciliation consuming engineering time on every billing cycle.

The Outcome

3,000+Users on platform

Automated billing, multi-tenant logic, and Stripe integration — all agent-managed.

LangGraphStripe APIPostgreSQL
HealthURAG-Powered Agent

The Problem

Clinical data trapped in documents, PDFs, and internal knowledge bases. Staff spending hours searching for answers that should be instant.

The Outcome

18Clinical queries answered per session

Agents reason over proprietary clinical data in real time. Full audit trail on every response.

LangChainPineconeGPT-4o
SupChainsSupply Chain Agent

The Problem

Demand signals processed manually. Reorder decisions dependent on a single analyst checking spreadsheets every morning.

The Outcome

ZeroStockouts this quarter

Autonomous demand monitoring across 47 SKUs. Reorder recommendations generated and routed without human involvement.

LangGraphPythonERP Integration

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Technology

The Stack That Produces the Best Results in Production.

We are model-agnostic and tool-agnostic. Every choice is made based on what produces the best outcome for the specific system being built.

Model-agnostic by design

We choose the best model for each task — GPT-4o, Claude, Gemini, or open-source. No vendor allegiance. No upselling a specific platform.

Built for your stack, not ours

Every agent connects to the tools you already use. We do not ask you to change your systems. We wire intelligence into them.

Self-healing in production

Agents that retry on failure, log every step, and escalate only when the situation genuinely requires a human. Built to run without babysitting.

You own everything

Code, agents, workflows, and IP transfer to you at handover. No licence fees for what we build. No lock-in. No dependency on Quixas to keep it running.

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Commonly built on LangGraph · LangChain · n8n · Pinecone · Python · GPT-4o · Claude · PostgreSQL

Questions Worth Asking Before You Build

The questions operations leaders ask us most often — answered directly.

What exactly is an AI agent and how is it different from a chatbot?

A chatbot responds to questions. An AI agent pursues a goal. When you give an agent a task, it plans the steps, executes them across your connected systems, handles exceptions, and reports back — without you managing each step. The difference is not subtle. One produces text. The other produces outcomes.

What kind of goals can an agent actually handle?

Any goal that involves a repeatable sequence of decisions, data lookups, and system actions. Qualifying a lead and updating the CRM. Processing an invoice end to end. Answering a complex query from your internal documents. Monitoring inventory and triggering reorders. If your team runs the same multi-step process more than a dozen times a week, an agent can almost certainly handle it.

How is a Quixas-built agent different from using ChatGPT or another off-the-shelf AI tool?

Off-the-shelf AI tools respond to prompts in isolation. They have no memory of your previous interactions, no access to your live systems, and no ability to take action on your behalf. A Quixas agent is connected to your actual tools, trained on your specific context, and designed to execute — not just respond. It operates 24/7 without being prompted for each step.

What is LangGraph and why do you use it?

LangGraph is an orchestration framework that lets us design agents as stateful graphs — each node is a decision or action, each edge is a possible path based on context and results. This makes agent behaviour deterministic, debuggable, and auditable. When something goes wrong in production, we can trace exactly what the agent decided and why. That matters when agents are running on live business data.

What happens when the agent makes a mistake or hits an unexpected situation?

Every agent we build includes a self-healing layer. When an action fails — a timeout, a bad API response, an unexpected data format — the agent retries with exponential backoff, logs the failure with full context, and escalates to a human only when the retry budget is exhausted. Most real-world errors are resolved automatically without any intervention from your team.

How long does it take to go from conversation to a live agent?

A single-function agent typically takes 2 to 4 weeks from approved design to production deployment. A multi-agent system covering several workflows takes 6 to 10 weeks. Every engagement begins with a free 2-week diagnostic where we map the target process, design the agent architecture, and estimate time and cost savings — before any build begins.

Do we need to replace our existing tools or systems?

No. We build agents that connect to the tools you already use — your CRM, ERP, inbox, calendar, databases. We do not ask you to change your stack. We wire intelligence into it. The agent becomes a layer on top of your existing systems, not a replacement for them.

What does it cost and what do we own at the end?

We work on a project basis scoped to your specific system. We offer a free strategy session where we estimate cost, timeline, and expected savings before you commit. At handover, you own 100% of the code, agent logic, and IP. No ongoing licence fee for the system we built. No lock-in.

Walk Away With a Blueprint.
Not a Sales Pitch.

In 30 minutes we map the process, design the agent, and show you exactly what it would cost and what it would save. You leave with a clear picture — whether you build with us or not.

Free strategy session
Response within 1 hour
No proposal until you're ready