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BarrioVibe
Software & AI

Agentic AI Development

An agent takes an objective, plans a route to it, calls your systems, and finishes the task. That is a different thing from a chatbot, which answers and stops. We build agents scoped to one specific job, such as triaging tickets, reconciling records or researching and drafting, with the evaluation set and the approval gates designed in from the start rather than added after a demo goes wrong.

Start a projectWhat’s included

Scope

What'sincluded

Everything below is in the standard engagement. Anything outside it is agreed in writing before the work starts, never after.

  • Use-case scoping, including a straight answer on what an agent will not do reliably
  • Agent architecture: tool design, memory, planning and retry behaviour
  • Integration with your existing systems, APIs and data sources
  • Retrieval over your own documents and knowledge base
  • Guardrails, human approval on irreversible actions, and an audit log of every call the agent makes
  • An evaluation suite that measures accuracy before and after every change

Who this is for

Built for three situations

  • Teams drowning in repetitive work
  • Companies with unstructured data
  • Businesses piloting AI properly

Process

How thisactually runs

  1. Feasibility

    We look at the task and tell you if an ordinary script or a rules engine would do it better and cheaper. Plenty of work does not need an agent.

  2. Prototype

    A working agent on a narrow slice of the real task, scored against a labelled evaluation set so quality is a number rather than an impression.

  3. Guardrails

    Error handling, cost limits, approval steps and full action logging added before the agent touches anything in production.

  4. Deploy and monitor

    Rolled out with monitoring on accuracy, cost per run and failure modes, and a rollback path written down before it is needed.

Deliverables

What you end up holding

  • A deployed agent integrated with your systems
  • An evaluation suite with a documented accuracy baseline
  • Audit logs of every action the agent takes
  • Source code and infrastructure configuration
  • An operating runbook covering failure modes and escalation

Next step

Tell us your situation and we will scope it.

Talk to us about it

Scope, fee and dates confirmed in writing before anything starts.

Questions

AboutAgentic AI Development

  • How is an agent different from a chatbot?

    A chatbot tells a customer what your refund policy says. An agent opens the order, checks it against that policy, issues the refund in your system and writes the action to a log. Because an agent changes real records rather than describing them, it needs tool access, guardrails and an audit trail, none of which a chatbot needs.

  • What about hallucinations and mistakes?

    They are a design constraint rather than a solved problem, and anyone telling you otherwise is selling. We manage them by grounding answers in your own data, limiting which tools the agent is allowed to call, requiring a human to approve anything irreversible, and re-scoring accuracy against a labelled evaluation set every time the system changes.

  • Which AI models do you build on?

    Whatever fits the task and your constraints on cost, latency and data residency. Claude, GPT and open models all have cases where they are the right answer. We build the integration layer so the model can be swapped without rewriting the agent.

  • Will our data be used to train models?

    Not under the enterprise API terms we build on, which exclude your data from training by default. If you have residency or confidentiality requirements that rule out hosted models entirely, we can architect around self-hosted open models instead.

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