Wolvanta

AI agent development services

AI agent development for work that can't wait

Wolvanta designs and builds AI agents that read requests, make decisions and take action across your business systems. Every agent ships with guardrails, an audit log and a clear point where a person takes over.

Plan your first agentHow an agent works

What is an AI agent?

An AI agent is software that uses a large language model to pursue a goal. It plans the steps, calls tools and APIs, checks the results and keeps going until the task is done or it needs a person. A chatbot answers questions; an agent takes actions.

  • 01

    Agents act, not just answer

    They update records, send messages and file documents inside the systems you connect.

  • 02

    Tools set the limits

    An agent can only use the systems and permissions you grant it, and nothing else.

  • 03

    People stay in charge

    You decide which actions run on their own and which wait for approval.

AI agents we build

Each agent is designed around one job your team does every day, measured against how that job is done today.

  • Support triage agent

    Reads every incoming ticket, tags and prioritizes it, drafts a reply from your knowledge base and routes edge cases to the right person.

  • Finance operations agent

    Matches invoices to purchase orders and receipts, flags mismatches and prepares entries for approval in your accounting system.

  • Sales research agent

    Researches target accounts, enriches CRM records and drafts first-touch emails for your SDRs to review and send.

  • Document processing agent

    Extracts fields from contracts, claims and forms, checks them against your rules and files the results where they belong.

  • IT and operations agent

    Handles password resets, access requests and routine incidents end to end, with every action written to the audit log.

  • Multi-agent workflows

    Specialized agents that plan, check and hand work to each other for longer processes, coordinated by a supervisor agent.

How an AI agent works

Every agent we build follows the same shape: a trigger starts it, it plans with your knowledge and tools, guardrails check each step, and people approve what matters.

How an AI agent worksA request arrives, the agent plans using your knowledge and tools, guardrails check every step, people approve sensitive actions, and every action is logged and monitored.TriggerNew ticket or emailScheduled jobForm or API callYour knowledgeDocs, policies, past casesAI agentPlans the stepsReasons over contextPicks the next toolYour toolsCRM, ERP, email, APIsGuardrailsPermissionsPolicy checksSpend limitsA personApproves or editsActionDone and loggedMonitoring: accuracy, cost, speed
A request arrives, the agent plans using your knowledge and tools, guardrails check every step, people approve sensitive actions, and every action is logged and monitored.

How we keep agents safe

An agent is only useful if you can trust what it does. These controls are part of every build, not extras.

  • Least-privilege access

    Each tool connection gets the narrowest permissions the job needs.

  • Approval steps

    Payments, deletions, external emails or anything you choose waits for a person.

  • Evaluation before release

    A test set built from your real cases scores every version before it ships.

  • Prompt-injection testing

    We attack the agent with hostile inputs before your users ever see it.

  • Full audit log

    Every decision, tool call and result is recorded and searchable.

  • Cost and rate limits

    Hard caps on spend and actions per hour stop a runaway loop.

Models, frameworks and integrations

We are model-agnostic: each task gets the model that meets your accuracy, speed, cost and data-residency needs.

Models
  • OpenAI GPT
  • Anthropic Claude
  • Google Gemini
  • Llama
  • Mistral
Agent frameworks
  • LangGraph
  • LlamaIndex
  • OpenAI Agents SDK
  • Model Context Protocol
Knowledge and memory
  • PostgreSQL with pgvector
  • Pinecone
  • Weaviate
  • Elasticsearch
Business systems
  • Salesforce
  • HubSpot
  • Zendesk
  • Slack
  • Microsoft 365
  • Google Workspace
  • NetSuite
  • QuickBooks
Cloud
  • AWS
  • Microsoft Azure
  • Google Cloud

From idea to a working agent

Typical timing for a first agent. Exact dates are agreed in the project plan.

  1. 1–2 weeks

    Discovery

    Pick one job, map its steps and systems, and set the success metric.

  2. 2–3 weeks

    Prototype

    The agent working on your real cases, scored against how the job is done today.

  3. 4–8 weeks

    Build

    Integrations, guardrails, approval steps, evaluation suite and security review.

  4. 1–2 weeks

    Launch

    Shadow mode first, then a phased rollout with monitoring in place.

  5. Ongoing

    Improve

    Weekly review of accuracy, cost and handoffs; new skills added as trust grows.

Ways to work with us on agents

Project delivery

One agent, scoped and shipped with fixed milestones.

Start here

Dedicated AI team

A managed pod that builds agents across your roadmap.

See how it works Soon

Hire AI engineers

Agent engineers who join and report to your team.

See how it works Soon

Related services

  • Generative AI and LLM appsSoon
  • AI automationSoon
  • Chatbots and voice agentsSoon
  • AI security and red teamingSoon
  • AI SDRSoon
  • Fractional CTOSoon

AI agent development: questions, answered

What is AI agent development?

AI agent development is the design, building and deployment of software agents that use large language models to complete tasks: planning steps, calling tools and APIs, and acting in business systems. It covers the agent's logic, its integrations, its guardrails and the monitoring that keeps it reliable.

How is an AI agent different from a chatbot?

A chatbot answers questions in a conversation. An AI agent works toward a goal: it can look up records, update systems, send messages and decide what to do next, with or without a conversation. Many agents also chat, but acting is what makes them agents.

What types of AI agents do you build?

We build autonomous agents for back-office work, conversational agents for customers and staff, decision-support agents that recommend actions to a person, and multi-agent systems where several specialized agents coordinate on a longer process.

How long does it take to build an AI agent?

A first agent typically takes 8 to 15 weeks: 1 to 2 weeks of discovery, 2 to 3 for a prototype on your real cases, 4 to 8 for the production build and 1 to 2 for a phased launch. Later agents are faster because the integrations and guardrails already exist.

How do you stop an AI agent from making mistakes?

We limit what it can touch, require approval for sensitive actions, test every version against a set of your real cases before release, attack it with prompt-injection tests and log every action. In production we monitor accuracy and send uncertain cases to a person.

Which models and frameworks do you use?

We choose per task among models from OpenAI, Anthropic, Google and open-weight families such as Llama and Mistral, and build with frameworks such as LangGraph, LlamaIndex and the Model Context Protocol. You are never locked to one vendor.

Do we own the AI agent and its code?

Yes. The code is in your repositories, the agent runs in your cloud accounts and its prompts, evaluation sets and documentation are yours. If you stop working with us, you keep all of it.

Plan your first AI agent

Tell us which job you'd hand to an agent and which systems it touches. We'll come back within one business day with how we'd approach it and what a prototype would prove.

We reply within one business day.

FreeEvery qualified enquiry gets a homepage redesign concept
Office
Your office address, City, State
Hours
Monday to Friday, 8am to 6pm Central
  1. Your project
  2. About you
What do you need?