AI agents that do the work, then hand off cleanly.
In short
An AI agent is a system that takes an objective and works towards it across several steps, retrieving data, calling your tools and APIs, and deciding what to do next, rather than answering a single question and stopping. The difference between a demo and a production agent is everything that surrounds those steps: permissions, retries, observability, cost ceilings, and a clean hand-off to a person when confidence is low. We build agents with those guardrails in place from the first day, and we grade them against an eval set before they touch a real queue.
What you get
What changes when this works.
Real actions, not suggestions
The agent writes to your systems through tools you approve: create the ticket, update the record, route the document, rather than producing text a human then re-types.
A hand-off you can trust
Low-confidence cases escalate to a person with the reasoning and sources attached, so the human starts from a briefed position instead of from scratch.
Observable by default
Every run is traced: what the agent saw, which tools it called, what it cost. When something goes wrong you can read the transcript instead of guessing.
Bounded cost and blast radius
Step limits, spend ceilings, and scoped credentials. An agent that loops should cost you a rounding error and touch nothing it shouldn’t.
Concretely
What we actually hand over.
No deliverable on this list is a document about the work. Each one is the work.
- Agent architecture with explicit tool definitions and scoped credentials
- Orchestration in LangGraph or an equivalent, chosen for your control needs
- MCP tool servers where your systems are worth exposing reusably
- Human-in-the-loop escalation path with confidence thresholds
- Tracing and cost telemetry (LangSmith or equivalent)
- Eval harness covering task success, tool-call correctness and refusal behaviour
In practice
A European insurance carrier
Our clients’ systems are commercially sensitive, so these stories are anonymised at their request. Metrics are reported as ranges or percentages rather than raw figures.
Problem
Claims analysts spent hours triaging inbound documents by hand: classifying, extracting key fields, and routing each one to the right queue.
Action
We built a RAG and agent pipeline that classifies, extracts and routes documents, with an eval harness grading extraction accuracy and a confidence threshold that escalates edge cases to an analyst.
Result
[X]% reduction in manual triage time; [X]% extraction accuracy on the held-out set.
“It shipped in nine days and we still run it daily. The escalation path is what made our team comfortable with it.”
Questions
AI agents, answered plainly.
What’s the difference between an AI agent and a chatbot?
A chatbot responds to a message and stops. An agent pursues an objective across multiple steps, calling tools and systems along the way, and decides for itself what to do next, so it can finish a task rather than describe one. The practical consequence is that an agent needs permissions, cost limits, and an escalation path, none of which a chatbot requires.
How do you stop an agent doing something expensive or wrong?
Three limits, all set before launch: scoped credentials so it can only touch the systems it needs, step and spend ceilings so a loop is a rounding error rather than an incident, and a confidence threshold that escalates to a person instead of guessing. Every run is traced, so an incident is something you read rather than reconstruct.
Do agents replace the people doing this work?
In the deployments we ship, they take the fetching, checking and routing and leave the judgement. The people doing the work usually become the reviewers of the edge cases the agent escalates. We would rather scope that honestly on Day 0 than have it discovered on Day 10.
Which frameworks do you use?
Usually LangGraph when a workflow needs explicit control over state and branching, and MCP for tool servers we want to reuse across systems. We will also tell you when a plain, well-tested pipeline beats an agent framework, a surprising number of “agent” problems are really three deterministic steps and one model call.
Related
The other four.
Most projects touch more than one of these. If you're not sure which yours is, that's what the scope call is for.
Start here
Scope your first workflow.
Tell us the process that eats your team’s afternoons. We’ll come back within one business day with a scope, a fixed price, and an honest answer about whether AI is the right tool for it.
- No sales sequence. One reply, from the person who’d build it.
- If we don’t think it’s worth building, we’ll say so.
Email us what the process is, roughly how often it runs, and who does it today. That is enough for us to come back with a scope.
Email usOr write to admin@vintrexlabs.com directly.
Prefer to talk
Book a 20-minute call.
Bring one workflow. We’ll tell you on the call whether it fits in 10 days, and roughly what it costs.
Pick a 20-minute slot
Opens our live calendar. Bring one workflow and we’ll tell you on the call whether it fits in ten days.
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