Who this is for
Ops, support, and engineering leads at SMB and mid-market teams
Design and ship agent-driven automations that replace repetitive knowledge work — triage, extraction, enrichment, routing — with measurable quality gates and human-in-the-loop checkpoints.
Outcomes
- Cut manual triage or extraction cost by 40–80%
- Guardrails and evals shipped alongside the agent, not bolted on later
- Operator dashboards so humans stay in control
What AI automation actually means
Most 'AI automation' pitches are thin wrappers around a single LLM call. Real automation means an agent loop with tool use, error handling, retries, escalation paths, and a metric the business actually cares about. WolfAI designs automations around that metric first, then picks models second.
Where it works well
Strong fits: inbound ticket triage, document extraction (invoices, contracts, forms), CRM enrichment, lead qualification, content moderation with human review, and internal Q&A over company docs. Weak fits: anything with tight real-time safety constraints or hard regulatory audit requirements without a human reviewer in the loop.
How we measure success
Every automation ships with an eval harness — a set of labelled examples the agent is judged against on every prompt or model change. That eval, not vibes, is the release gate.
Related products
Models typically involved
Frequently asked questions
What is an AI automation agent?
An AI automation agent is an LLM plus a set of tools (APIs, databases, document parsers) plus a control loop that decides which tool to call next. Well-built agents include retries, escalation, and guardrails so they can run unattended in production.
Which models are best for agent workflows?
Claude Sonnet 4.6 and GPT-5.5 are strong general agent models. Claude Haiku 4.5 is cost-effective for narrow extraction and routing tasks. Claude Opus 4.7 is reserved for the hardest reasoning steps, usually gated behind a router.