Agentic AI Systems

AI that doesn't just respond. AI that acts.

Most teams don't need another chatbot. They need a system that takes a trigger, reasons through a multi-step process, acts on the result, and closes the loop without a human in the middle. That's what we build: multi-step agents that pull data, make decisions, trigger actions, and hand off to other systems.

What an Agent Actually Is

An agent is not a chat interface with memory. It is a system that can:

• Read from a source (email, CRM, database, API, file store)
• Make a decision based on context and rules
• Take an action (write, send, update, notify, escalate)
• Hand off to another agent or wait for a condition

We use LangGraph for stateful, multi-agent workflows where the logic branches. LangChain for simpler sequential pipelines. MCP (Model Context Protocol) for giving agents structured access to external systems. The stack is picked by the problem, not the other way around.

What We Build

• Autonomous lead research and enrichment agents. Pull from LinkedIn, company sites, and data providers; score; push to the CRM.
• Document processing and data extraction pipelines. Ingest contracts, invoices, and reports; extract structured fields; route.
• Multi-agent orchestration for complex operational workflows. Specialist agents with defined roles, handoffs, and shared state.
• AI-powered internal operations assistants. Agents connected to your tools that actually execute tasks, not field questions.
• Automated reporting and insight generation. Pulls from data sources, summarizes, flags anomalies, delivers on schedule.
• Customer onboarding automation. Account setup, data collection, verification, welcome sequences, first-touch scheduling.

When You Need This

• Your team manually reviews and routes inbound requests every day.
• A process requires reading multiple sources, making a judgment, and writing a result.
• You have a workflow that runs in sequence across more than two tools.
• You need AI to act on a schedule or a trigger, not on a button click.

What We Don't Build

We don't wrap an LLM API, call it an agent, and charge for it. We don't use "agentic" to describe a chatbot with memory. When we say multi-agent, we mean multiple distinct agents with defined roles, handoffs, and state, running in production.

Stack

• Orchestration: LangGraph (stateful, branching), LangChain (sequential), OpenAI Agents SDK, Pydantic AI
• Models: Claude Sonnet, GPT-4o, Gemini 2.5 Pro, Llama 3.3 (self-hosted via Ollama), scoped to the task
• Protocol: MCP server implementations for structured tool access
• Memory & retrieval: Pinecone, Qdrant, Supabase pgvector, PostgreSQL
• Infra: AWS, Azure, or self-hosted, depending on compliance requirements

How We Scope It

We map the current workflow first: what triggers it, what decisions are made, what systems are touched, where the bottleneck is. Then we design the agent architecture: which parts need reasoning, which are deterministic, where human review still makes sense. We don't automate decisions that shouldn't be automated.

Selected Work

• B2B AI Leads Automation: n8n pipeline with Groq-based scoring agent that enriches leads via Apollo.io and only sends outreach when the fit score clears threshold.
• AI Client Conversation Router: GPT-4o-mini agent that triages inbound client conversations, routes department emails, logs to HubSpot, and alerts Slack and WhatsApp from a single webhook.
• Customer Health & Churn Alert: Dual n8n pipeline that scores ticket sentiment and evaluates HubSpot deals against churn signals with GPT-4o-mini, alerting CSMs before renewal windows close.

Start Here

Book a 15-minute audit. We'll map the workflow, point at the highest-ROI agent to build first, and hand you a prioritized roadmap. No commitment to proceed.

Book a 15-minute audit →