AI Agent Development · Dallas, TX
We Build AI Agents That Do the Work
AI agent development is the process of designing, building, and deploying autonomous software agents that reason through tasks, take actions, and coordinate with other systems without requiring step-by-step human instruction. Unlike a workflow that executes a fixed sequence of steps, an AI agent evaluates context, selects the appropriate tools, and decides what to do next based on the goal it has been given. A single agent can research a lead, qualify it against your criteria, draft a follow-up message, and create a CRM record, without a human touching any part of the process. The output is infrastructure that operates independently, not a chatbot you have to babysit.
Example Agent Invocation
{
"agent": "lead-operations",
"task": "qualify_and_route",
"input": { "lead_id": "f29a3e..." },
"tools": ["crm.read", "scoring.predict", "email.draft"],
"memory": "qdrant://agent_accounts"
}A real agent invocation from our production system.
Free 30-min call · No commitment required
The Gap
What Most AI Services Get Wrong
Most AI services being sold today are prompt chains dressed up as infrastructure. Someone has connected ChatGPT to a Zapier workflow, wrapped it in a Notion template, and put it on a landing page. That is not an AI agent. It is a fancy form.
Real AI agents have persistent memory, access to real data through structured tool integrations, and the ability to hand work to other agents when a task exceeds their scope. They do not hallucinate a response and call it a deliverable. They call your CRM API, read the actual record, and take the next logical action.
The gap matters because prompt-grade automation breaks under real conditions. It does not know what happened last week. It cannot check whether the invoice was paid before sending the onboarding sequence. It has no way to route a complex inquiry to a specialist agent. You get one shot, and if the context was wrong, the output is wrong.
We build production-grade systems. That means n8n for orchestration, Model Context Protocol (MCP) servers for structured tool access, Qdrant for persistent vector memory, and specialized agents that hand work to each other based on task type. Not a prompt in a Zapier step.
What We Build
Four AI Agent Systems We Ship
Purpose-built agents scoped to your operations, not generic bots you have to configure yourself.
AI Lead Operations Agent
Handles everything from the moment a lead comes in: intake, qualification, CRM record creation, routing to the right rep, and follow-up sequencing. If no one responds within a defined window, it escalates. We built this system for GOAT Home Services and got their lead response time under five minutes. It runs automatically across every channel they use for lead gen.
AI Account Manager Agent
Connects to your CRM and billing system, monitors deal stages, drafts proposals and SOWs based on your templates, sends contracts for signature, and logs every interaction automatically. For clients with recurring engagements, it tracks what is due, surfaces renewal windows before they close, and flags accounts that have gone quiet.
AI Content Operations Agent
Manages the full content pipeline: calendar planning, drafting by platform, scheduling, and publishing. The agent reads your brand voice rules, accesses your content history through semantic memory, and produces drafts that do not require a full rewrite. We run this system to operate content across four brands, publishing three days a week without a content manager.
Custom Agent Builds
If your use case does not fit one of the three above, we scope it from scratch. We have built agents that manage vendor communications, triage support tickets, monitor competitive signals, and coordinate multi-step research workflows that previously required a full-time analyst.
Photo: Pexels
Photo: Pexels
Built First, Sold Second
The Same System That Runs Whtnxt
We did not build a demo. We built the operational system that runs the Whtnxt consultancy, then productized the approach for clients.
The current stack: 13 specialized agents (Content Director, Account Manager, Growth Strategist, Marketing Technologist, and nine others), each with a defined scope and a set of executable skills. They are coordinated by an orchestrator that routes incoming requests to the right agent based on task type. The agents share a persistent memory layer built on Qdrant and Obsidian, so context carries across sessions instead of starting from zero every time.
The infrastructure includes 10+ custom MCP servers that connect the agents to real business systems: CRM, billing, email, contracts, analytics, content publishing, and more. When the Account Manager agent drafts a proposal, it is reading live deal data from Twenty CRM and pulling pricing from InvoiceNinja. When the Content Director schedules a post, it is writing directly to LinkedIn via a custom MCP integration and logging the record to NocoDB.
This is not vaporware. It is the system running this business today. What we build for clients is a version of it scoped to their specific operations.
13
Specialized agents
50+
Executable skills
10+
Custom MCP servers
How We're Different
Three Things That Separate Production AI from Prompt Chains
Multi-Agent Orchestration, Not Single-Prompt Automation
A single LLM prompt is a one-shot operation. A multi-agent system is a workflow where specialized agents hand work to each other. One agent researches a lead. Another qualifies it. A third drafts the outreach. The orchestrator coordinates all of them without any human involvement between steps. That is where AI infrastructure is going in the next 24 months. We are already operating this way.
Production Infrastructure, Not Prompt Chains
n8n handles event-driven orchestration. MCP servers provide structured, authenticated access to business tools. Qdrant stores vector memory that persists across sessions. Local fine-tuned models handle routine classification tasks without API cost. This is not a ChatGPT plugin. It is infrastructure built on the same principles as any serious software system.
We Built Ours First
Every pattern we recommend to clients, we stress-tested on our own operations first. When we say a lead operations agent can get response time under five minutes, we know because we built one and it runs every day. When we say a content operations agent can manage a four-brand publishing calendar, we know because ours does. The reference implementation is live.
Pricing
What AI Agent Development Costs
Agent builds start at $5,000 for a focused single-agent system with defined scope and integrations. Retention and maintenance starts at $1,500/month for ongoing operation, monitoring, and iteration. Complex multi-agent systems with custom MCP servers and memory infrastructure range from $15,000 to $25,000 depending on the number of integrations and agents involved.
All engagements are sprint-based with no long-term contracts. Full pricing on the pricing page.
FAQ
AI Agent Development FAQ
What is AI agent development?
AI agent development is the process of designing and building autonomous software agents that reason through a task, select the appropriate tools, and take action without step-by-step instruction. The agents connect to real business systems through tool integrations and can coordinate with other agents when a task requires multiple capabilities. The result is infrastructure that handles multi-step operational work end-to-end.
What is the difference between an AI agent and AI automation?
Traditional automation follows a fixed script: when X happens, do Y. It does not evaluate context, it does not adapt to new information, and it cannot handle scenarios you did not anticipate when you built the workflow. An AI agent reasons about what to do next. It reads context, selects tools, and can decide to route a task to a different agent if the request is outside its scope. The distinction matters most when your workflows involve ambiguity, exceptions, or multi-step coordination.
How much does AI agent development cost?
Focused single-agent builds start at $5,000. Retention and maintenance starts at $1,500 per month. Multi-agent systems with custom infrastructure range from $15,000 to $25,000. The main cost drivers are the number of tool integrations, whether custom MCP servers need to be built, and the complexity of the agent orchestration logic.
How long does it take to build a custom AI agent?
A focused single-agent build with defined scope and existing API access typically takes two to four weeks from kickoff to deployment. Multi-agent systems with custom integrations typically run six to ten weeks. Timeline depends heavily on how well-documented your existing systems are and how quickly we can get API access during the build phase.
Will the AI agent integrate with our existing tools?
Yes, and that is specifically what MCP servers are for. We build custom MCP servers that give AI agents structured, authenticated access to your CRM, billing system, email platform, and any internal API that has documentation. If your tool has an API, we can connect an agent to it. For tools without APIs, we build browser-based automation bridges. See the MCP development page for specifics.
Book a Free AI Agent Strategy Call
We will review your current operations, identify where agent automation creates the most leverage, and show you what a purpose-built system would look like for your business. No pitch, no commitment.
Book a Free AI Agent Strategy CallFree 30-min call · No credit card required · No commitment