AI Agent Development,
Built for Production
Hire AI developers who build autonomous agents that replace your most expensive manual workflows: invoice processing, lead qualification, support triage, compliance checks. Systems that integrate into your stack, run 24/7, and return measurable ROI within 30 days.
Software that does the work, not software that reminds you to do it.
If your team spends its days chasing invoices, qualifying the same kind of lead over and over, answering the same support questions, or checking documents against a rulebook, that work is predictable enough to hand to a machine. An AI agent is simply a program that does the whole job start to finish, rather than a tool your staff has to sit and operate.
The difference between an agent and a chatbot matters here. A chatbot answers a question. An agent reads your systems, makes a decision, does the task, and writes the result back where it belongs. Nobody has to copy anything from one screen into another.
Most businesses looking to hire AI developers do not actually want AI. They want a specific job to stop consuming a specific number of hours every week. That is the conversation we start with.
What you actually get
- ✓ One expensive manual workflow picked, built, and running in production
- ✓ It connects to the systems you already use, so nothing changes for your team
- ✓ A written before-and-after showing exactly what it saved you
- ✓ Documentation and handover, so you are never locked in to us
- ✓ Thirty days of support after it goes live
Everything below this point is the technical detail: the frameworks, models, and architecture we use. If that is not your area, skip it and send us a note instead.
Seven agent engineering capabilities.
Each built on the production frameworks that power real business workflows — not demos. We select the right framework for your use case, not the one we happen to prefer.
OpenAI Agents SDK & Swarm Systems
We build multi-agent systems using the OpenAI Agents SDK — the production-ready evolution of OpenAI Swarm. These systems orchestrate teams of GPT-5.6 Sol and Terra agents that delegate tasks, enforce guardrails, and execute parallel workflows with full tracing via LangSmith.
- ✓ Multi-agent handoff orchestration
- ✓ Built-in guardrails & tracing
- ✓ Compatible with 100+ LLMs
Anthropic Claude Agent & MCP Integration
Using the Anthropic Claude Agent SDK alongside the Model Context Protocol (MCP), we build agents that natively connect to your live business tools like Notion, Salesforce, and internal APIs. Claude Fable 5 agents handle complex, multi-session workflows that previously required entire teams.
- ✓ Native MCP server connections
- ✓ Claude Fable 5 multi-session memory
- ✓ Slack, Asana & Jira integrations
LangGraph Stateful Agent Workflows
For complex, decision-tree workflows requiring conditional logic and rollback capabilities, we engineer with LangGraph. Its graph-based execution model maintains explicit state over long-running processes, enabling agents to loop, retry, and adapt in real time across your enterprise systems.
- ✓ Stateful multi-step execution
- ✓ Conditional branching & retry logic
- ✓ Production deployment via LangGraph Cloud
CrewAI Role-Based Agent Teams
We design CrewAI multi-agent systems where distinct agents play specialised roles: Researcher, Analyst, Writer, and QA. This crew-based approach mirrors real team structures and is ideal for content pipelines, market research automation, and sales workflow orchestration.
- ✓ Role-based agent specialisation
- ✓ Parallel task execution
- ✓ Integration with LangChain tools
Google ADK & Vertex AI Agents
For enterprises within the Google Cloud ecosystem, we build with the Google Agent Development Kit (ADK) backed by Gemini 3.7 Flash and Gemini 3.1 Pro. ADK enables hierarchical agent compositions and custom tool execution at cloud scale via Vertex AI Agent Engine.
- ✓ Gemini 3.1 Pro / 3.7 Flash models
- ✓ Vertex AI production deployment
- ✓ Hierarchical agent compositions
On-Premise & Private Cloud Agents
When data cannot leave your environment, we deploy agents using open-weight models like Llama 4 or Mistral Large running on your own GPU infrastructure. Combined with a local Pinecone or pgvector RAG store, your agents operate completely air-gapped.
- ✓ Llama 4 & Mistral Large deployment
- ✓ Local vector DB (pgvector / Qdrant)
- ✓ Zero data egress guarantee
SKILL.md Agentic Engineering
We lead the shift toward agentic competence with the 2026 SKILL.md open standard. By packaging workflows into modular, reusable skills, we ensure your agents are hyper-specialised, portable across frameworks, and context-efficient via progressive disclosure of expert knowledge.
- ✓ Standardized SKILL.md packaging
- ✓ Progressive disclosure architecture
- ✓ Cross-framework skill portability
What we build this on.
The platforms and models behind this service. We are vendor-neutral: each piece is chosen because it is the right fit for the job, and we move you when something better ships.
New models ship constantly. Keeping your systems current as they do is part of the retainer, not a change request you get billed for.
Agents that run in production, not just in demos.
We combine the OpenAI Agents SDK, LangGraph, and Anthropic MCP to build agent systems that integrate deeply, scale reliably, and deliver documented ROI in weeks.
We build it, then we keep it running.
What we deliver does not get handed over and forgotten. For one monthly fee we run it, fix it, adjust it, and move it onto newer AI models as they ship. No separate maintenance contract, no charge every time you want something changed.
We run what we built
Monitoring, alerting, and fixing it when something upstream changes.
Updates are free
Small changes and tweaks carry no development fee. You ask, we do it.
Model upgrades included
We move you to newer models when they ship. No migration fee.
Fixes aren’t billable
If something we built breaks, repairing it is not a new invoice.
Support on your channel
Slack, Teams, WhatsApp, email. Priority response under four hours.
New automation every sprint
Each month adds working automation, so the return compounds.
If we built it, keeping it working and keeping it current is already paid for.
Frequently asked questions.
What is an AI agent, and how is it different from a chatbot?
An AI agent is an autonomous software system that can plan, make decisions, and take actions across multiple tools and systems to complete a goal. Unlike a chatbot that only responds to messages, an AI agent can read your CRM, update your project management tool, send emails, query databases, and orchestrate multi-step workflows independently. We build agents using OpenAI Agents SDK, LangGraph, and Claude MCP that integrate directly with your business tools.
Which AI agent framework should I use — OpenAI Agents SDK, LangGraph, or CrewAI?
It depends on your use case. OpenAI Agents SDK is ideal for multi-agent orchestration with built-in guardrails and tracing. LangGraph excels at complex, stateful workflows with conditional branching and retry logic. CrewAI is best for role-based agent teams that mirror real team structures. Claude MCP is strongest when you need agents that natively connect to live business tools like Slack, Notion, and Jira. We evaluate your requirements and recommend the right framework — or a combination.
How long does it take to build and deploy a custom AI agent?
A focused single-agent MVP typically takes 2–4 weeks from kickoff to deployment. More complex multi-agent systems with multiple tool integrations, custom RAG pipelines, and production monitoring usually take 6–10 weeks. Every engagement starts with a paid discovery phase where we map your workflows and deliver a scoped implementation plan before building.
How much does AI agent development cost?
Almost everyone starts with our $5,000 pilot: one fixed-scope workflow, built end to end, with the before-and-after numbers measured. That is the entry point for every service we offer. If the pilot proves out and you want a full build, a typical single-agent system runs $15,000–$25,000, and multi-agent enterprise systems with custom RAG, monitoring, and compliance requirements range from $40,000–$100,000+. You always get a fixed price before committing to a full build.
Is my data secure when using AI agents?
Absolutely. For sensitive environments, we deploy agents using open-weight models like Llama 4 or Mistral Large running on your own infrastructure with local vector databases — zero data egress. For cloud deployments, we use Azure OpenAI Service or AWS Bedrock with encrypted data at rest and in transit, audit logging, and PII redaction pipelines. All solutions align with SOC 2, HIPAA, and GDPR frameworks.
Thirty minutes.
We'll tell you exactly
where your ROI is.
No sales deck. No 50-page report you have to pay for before anything gets built. Just a direct conversation about which of your workflows are costing the most and whether AI can fix them. If there's no compelling answer, we'll say so. And it's a conversation with Kash, our founder, not a rep reading from a script, because the person who built this business is the one who should understand yours.