D I Z I T A L O G I C

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Dizitalogic is an AI first software company. We build agents, retrieval systems and the full stack products they run inside, for clients across six markets. Twenty plus engineers, working in your stack and your timezone.

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AI Agents and Automation

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AI Agents and Automation

We build agent systems that carry real work: an agent plans a task, calls your tools, hands off to another agent when it should, and stops for a person when the cost of being wrong is high.

This is engineering, not prompting. Orchestration in LangGraph, tool calls against your real APIs, evaluation sets before anything ships, and a full trace of every model call and tool response so you can see exactly what the system did and why.

What we build

Agent systems that survive contact with production.

  • Multi agent orchestration in LangGraph, with explicit handoffs
  • Tool calling against your own APIs, with retries and guardrails
  • Human approval steps wherever being wrong is expensive
  • Full tracing of every model call, so failures are debuggable

Who it suits: teams with a repetitive process that spans several systems, where a person is currently the integration layer. Tools we use: OpenAI, Anthropic Claude, Google Gemini, Llama, Mistral, LangChain, LangGraph, n8n, Make, AWS Bedrock and Azure AI.

Common questions

The questions we get asked most before a project starts. If yours is not here, ask it directly and an engineer will answer.

A chatbot answers. An agent acts. It calls your systems, changes records and completes a task, which is why guardrails, approval steps and tracing matter far more than the conversation itself.
It is designed to. Agents run against an evaluation set before release, high risk steps pause for human approval, and every tool call is logged so you can see the decision path rather than guess at it.
Not at first. We build against a sandbox or a read only copy, prove the behaviour, then widen access step by step. Nobody should hand an agent write access to production on day one.
Two to three weeks to a working prototype on your own data. Six to twelve weeks to something handling real volume, and the variable is almost always how many systems it must reach into.