Chatbots answer questions. Agents do work — they plan a multi-step task, use your tools and systems to carry it out, and report back with a trail you can audit. This is the most consequential shift in applied AI right now, and the teams wiring it into their operations early are banking a real advantage.
What an agent can take off your team’s plate
Operations workflows. Triage the inbound request, look up the account, apply the policy, execute the routine case end to end — and hand the judgment calls to a human with everything pre-gathered.
Document pipelines. Read the contract, extract the terms, cross-check against the system of record, flag mismatches, file the result — the work described in our document automation page, run by an agent instead of a queue of humans.
Research and monitoring. Watch the sources that matter, pull what changed, synthesise the digest your team actually reads.
Internal automation. The multi-step checklists — onboarding, provisioning, month-end — that eat hours precisely because they span five systems that do not talk to each other. Agents are good at “five systems that do not talk to each other.”
The part that makes it production-grade
Anyone can demo an agent. Running one against real systems demands the discipline most demos skip, and it is where we spend most of the engineering:
- Permissions first. The agent gets minimal, explicit access per tool — it cannot touch what you have not granted, by construction rather than by prompt.
- Checkpoints where stakes are high. Irreversible or expensive actions pause for human approval, with the agent’s reasoning and evidence presented for a ten-second review instead of a ten-minute investigation.
- Audit everything. Every step, tool call and decision is logged. When someone asks “why did it do that?”, there is an answer.
- Evaluate like software. Agents ship with scenario suites — including adversarial ones — that every change must pass. Reliability is a measured number, not a vibe.
We also wrote a plain-English primer on the concepts: AI agents explained.
How an engagement runs
We start with one workflow, not a platform: pick a high-volume, rule-guided process, define success numerically, and ship a permissioned agent for it in weeks. The first agent proves the pattern and builds the oversight muscle; the second and third get dramatically cheaper because the guardrail infrastructure already exists. It is the same prototype-then-production shape as all our custom AI work — and if you want the capacity in-house instead, we embed.
Have a workflow that eats hours every day? Tell us about it — we will give you an honest read on whether an agent is ready to own it.
Where this sits on the capability ladder
Agent systems are the second rung of how teams typically grow with us: start with an AI-native product build, add agents as workflows mature, then graduate to fine-tuned models and edge & on-device AI when the product earns it. Same team the whole way up.