Customer support agents
Resolve order, billing and account requests end to end, with refunds and exceptions routed for approval.
AI agents · since chatbots in 2017
AI agents that finish the job, not just answer the question
We build agents that read requests, check your systems, take the next action and ask a person when it matters, across chat, email and voice.
In short
What is an AI agent?
An AI agent is software that uses a large language model to plan and complete a multi-step task, such as resolving a support ticket or updating a CRM, by calling your tools and data. Unlike a chatbot, it takes actions, works within guardrails you set, and escalates to a person when it is unsure.
Where agents pay off first
Agents for the work your team repeats every day
Sales and lead agents
Qualify inbound leads, answer product questions, book meetings and keep your CRM up to date.
Operations agents
Triage emails, extract data from documents, reconcile records and chase missing information.
Voice agents
Answer and route calls, confirm bookings and capture details, with a clean hand-off to your team.
Internal knowledge agents
Answer staff questions from your documents, tickets and wikis, with sources for every answer.
Agent upgrades
Turn an existing rule-based chatbot into an agent that can act, without starting from scratch.
How we build agents
Safe, measurable agents in weeks
- Step 1
Pick one workflow
We map a single high-volume task, its systems and its risks, and define what success looks like.
- Step 2
Prototype on real data
A working agent in a sandbox within days, tested against your historical requests.
- Step 3
Add guardrails
Permissions, approval steps, evaluation sets and audit logs before anything touches production.
- Step 4
Launch, measure, expand
Go live on a share of traffic, track resolution and accuracy, then extend to the next workflow.
Agents vs chatbots
What changes when a chatbot becomes an agent
| Criteria | Rule-based chatbot | AI agent |
|---|---|---|
| Understands free-form requests | Limited | Yes |
| Takes actions in your systems | No | Yes, with permissions |
| Handles multi-step tasks | No | Yes |
| Knows when to ask a person | Fixed rules | Confidence-based hand-off |
| Improves from feedback | Manual rule edits | Evaluation and tuning |
FAQ
AI agent questions
How long does it take to build an AI agent?
A focused agent for one workflow typically reaches a production pilot in four to eight weeks, including integration, guardrails and evaluation.
Which AI models do you use?
We choose per task from leading commercial and open models, including Claude, GPT and open-source models, and can run on your cloud for data residency.
How do you stop agents from making mistakes?
Agents only get the permissions they need, risky actions need approval, every action is logged, and we test against evaluation sets built from your real cases before and after launch.
Can agents work with our existing tools?
Yes. We integrate with CRMs, helpdesks, ERPs such as NetSuite, eCommerce platforms, email, Slack, WhatsApp and custom APIs.
Read more
Guides on AI agents
AI agents
Harness Engineering: How to Make AI Agents Reliable
Harness engineering is designing everything around the model so AI agents work in production: context, tools, guardrails, tests and feedback loops.
4 min read
AI agents
How AI Agents Automate Sales, Support and Operations
Real workflows where AI agents qualify leads, resolve tickets and run back-office tasks, plus how to roll them out safely with people in the loop.
3 min read
AI agents
AI Agents vs Chatbots: Which Does Your Business Need?
Chatbots answer questions; AI agents finish tasks. See how they differ, where each fits in customer support, and when it is time to upgrade.
3 min read
Which task should your first agent take over?
Bring one repetitive workflow. We will show you what an agent could handle, what stays human, and what it takes to launch safely.