AI agents
A chatbot answers. An agent acts. Ours reason through multi-step goals, call your ITSM, CRM and ERP to get the work done, and hand over to a person the moment they should.
LangChain · LlamaIndex · OpenAI Assistants · Azure AI · Bedrock
Architecture overview · 5 layers
AI agents architecture
Five layers between a request arriving and an action being committed in your systems.
Interaction channels
A request arrives carrying who sent it, what they asked before, and which system it came from. That context goes with it into the next layer.
Agent orchestration engine
The goal is broken into steps. The agent works out which tool each step needs, what to retrieve, and when to stop and ask a question.
Tools, APIs & knowledge
Each step runs: a ticket is opened, a record is looked up, an article is retrieved. The result decides what the agent does next.
Guardrails & safety
Before anything is sent or committed, it is checked. Whatever sits outside scope or below the confidence threshold routes to a person.
Agent-powered outcomes
The response goes back or the action completes, and what happened feeds the metrics that shape the next version of the agent.
Key capabilities
What we design and run
Agent design & scope
Goals, task boundaries and human-in-the-loop rules: when an agent acts alone, when it asks approval, and when it hands over.
Orchestration & tools
Planning, memory and tool selection on LangChain, LlamaIndex, OpenAI Assistants, Azure AI or Bedrock, turning a goal into executable steps.
Conversation & UX
Chat, voice, email, Slack, Teams, mobile and webhook triggers, with the same context wherever the request arrives.
Tools, APIs & knowledge
ITSM, CRM and ERP APIs, knowledge bases, database queries, web search, documents and custom functions, so agents act rather than suggest.
Guardrails & safety
Input validation, output checks, PII detection, scope boundaries and rate limiting, with an audit trail that shows why an agent did what it did.
Evaluation & tuning
Resolution rates, response quality, escalation frequency and satisfaction, tracked so scaling decisions rest on outcome data.
Infrastructure & operations
Kubernetes, GPU and cloud compute, vector stores and message brokers, with Grafana and ELK watching agent behaviour as closely as any other service.
Agent-powered outcomes
Ticket triage and resolution, IT helpdesk automation, lead qualification, workflow automation and compliance checks, each scoped before it goes live.
Technology stack
Frameworks and infrastructure
One interface sits in front of the models, so a change of provider is a configuration edit.
Models and frameworks
OpenAI
Anthropic
LangChainAutoGPTRuntime and infrastructure
PythonNode.jsDocker
Kubernetes
PineconeUse case · Logistics
Carrier operations handled by agents
A logistics company handed carrier communication, rate negotiation and scheduling to agents, leaving coordinators to work the exceptions instead of the queue.
Frequently asked questions
A scripted chatbot answers what it was told to answer. A Gen AI assistant writes text. An agent reasons through a multi-step goal, picks and uses tools and APIs, takes real action in your systems, keeps memory across a conversation and knows when to hand over to a person. It resolves the ticket rather than describing how to.
Name the queue you want cleared
Tell us which repetitive requests your team handles most and which systems they touch. We will come back with an approach and a realistic shape for the work.