AI & Automation
AI Agents
AI agents that orchestrate multi-step workflows, use tools and APIs, and involve humans when decisions require oversight.
Talk to us about AI AgentsAn AI agent is software that takes a goal, decides the steps, calls the tools and systems it needs, and reports back. A chatbot answers a question. An agent finishes a piece of work: it reads the ticket, looks up the order, drafts the reply, updates the record and asks a person before it does anything risky.
Most agent demos work once, on a clean example. We build agents for the other days: when a tool times out, when the request is something the agent must not do, when the model picks the wrong function with arguments that look right. That difference is engineering, not prompting.
Problems this solves
- Skilled people spend hours on multi-step work that follows the same pattern every time: triage, lookups, data entry, first drafts.
- Work stalls between systems because a person has to copy information from one tool into another.
- An agent prototype exists, but nobody trusts it enough to let it touch real customers or real data.
- Volume is growing faster than the team that handles it.
What you get
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A scoped job for the agent
One clearly bounded piece of work with a defined start, end and success test, chosen with you before anything is built.
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Tools and integrations
Typed, tested connections to the APIs, databases and internal systems the agent needs, each with its own permissions and limits.
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Guardrails and human approval
Rules for what the agent may never do, and approval steps where a person confirms before money moves, data changes or a customer is contacted.
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State, retries and failure handling
The agent can resume after an error, does not repeat an action twice, and hands over to a person with full context when it is stuck.
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Evaluation and monitoring
A test set built from your real cases, plus logs of every step, tool call and cost, so quality is measured and not assumed.
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Documentation and ownership
The code, prompts, test set and infrastructure are yours, in your repositories, with a written guide for your team.
Is this the right fit?
A good fit when
- The work has clear steps and a clear definition of done.
- The systems involved have APIs, or can be given one.
- A wrong answer can be caught by a check or by a person before it costs you.
Probably not when
- The task is a single question and answer. That is a chatbot, and simpler.
- The steps never change. Plain automation is cheaper and more predictable.
- Nobody can say what a correct result looks like yet. Start with consulting.
How the work runs
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01
Understand
A short call to understand the actual problem, not just the requested feature. We push back if the scope doesn't match the goal.
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02
Architect
A concrete technical plan and estimate before any code is written, covering stack, integrations, and where the real risk is.
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03
Build
Iterative delivery with visibility into progress, not a black box until launch day.
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04
Validate
Tested against real-world requirements and failure modes, not just the happy path, before anything reaches production.
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05
Operate
Deployed, monitored, and supported after launch, with infrastructure and observability included from day one. Never a forced retainer.
Questions about AI Agents
What is the difference between an AI agent and a chatbot?
A chatbot answers questions in a conversation. An agent carries out a task across several steps and systems, such as reading a request, fetching data, taking an action and confirming the result. Many products use both: a chat interface in front, an agent doing the work behind it.
Can an agent act without a person approving it?
It can, but we decide that per action. Low-risk, reversible steps can run on their own. Anything that moves money, deletes data or speaks to a customer in your name usually gets a human approval step first.
Which AI models do you use?
We choose the model for the job and keep the design independent of one vendor where that is practical, so a model can be swapped when price, quality or policy changes.
How do you know the agent is good enough to launch?
We build a test set from real examples of your work before launch and run the agent against it on every change. You see the pass rate and the failures, not just a demo.
Can the agent work with our existing software?
Yes. Most agents we design sit on top of systems you already run. If a system has no API, we look at adding a small one before the agent work starts.
From the blog
What an AI Agent Costs to Run and How to Keep It Predictable
Agent costs are driven by steps, context size and model choice, not by the number of users. Each can be measured and limited.
How to Test an AI Agent Before It Touches Real Customers
A demo proves an agent can work once. A test set built from real cases shows how often it works, and where it does not.
AI Agent Guardrails: What to Restrict and What to Approve
An agent that can act needs limits that do not depend on the model behaving. A practical way to sort actions into free, approved and forbidden.