AI Agent Development &
LLM Integration
We build autonomous AI 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.
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-4o and GPT-o3 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 Opus 4.5 agents handle complex, multi-session workflows that previously required entire teams.
- ✓ Native MCP server connections
- ✓ Claude Opus 4.5 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 2.0 Flash and Gemini 2.0 Pro. ADK enables hierarchical agent compositions and custom tool execution at cloud scale via Vertex AI Agent Engine.
- ✓ Gemini 2.0 Pro / 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 3.3 70B or Mistral Large 2 running on your own GPU infrastructure. Combined with a local Pinecone or pgvector RAG store, your agents operate completely air-gapped.
- ✓ Llama 3.3 & Mistral Large 2 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
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.
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?
A typical single-agent MVP starts around $15,000–$25,000. Multi-agent enterprise systems with custom RAG, monitoring, and compliance requirements range from $40,000–$100,000+. We scope every project during a paid discovery engagement so you 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 3.3 or Mistral 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 “AI readiness assessment.” 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.