Build AI agents on Claude that act inside your systems
Build AI agents on Claude that act inside your systems.
Most of our clients want one number to move, the hours given back to staff each week, with the work still done correctly. A Claude-based agent gets there by reading a request, looking things up in your own records, taking a few defined steps, then finishing the task or handing it to a person almost complete. Claude earns its place here because it follows instructions closely, calls your tools in a sensible order, and holds its reasoning together across many steps. That reliability is what turns a clever demo into capacity you can count. We make it real by grounding the agent in your data, versioning every prompt and tool, and testing on your own past cases first.
Book a discovery callWhat we build on Claude
Reliable tool-using agents
Agents that call your APIs, search your systems and update records through Claude's tool use, taking actions in a sensible order rather than only describing what should happen.
Long-document reasoning
Agents that read across a full contract, policy set or case file in one pass, so the answer is grounded in your material instead of a lossy summary of it.
Tenancy-aware deployment
Where data rules require it, we run Claude through Amazon Bedrock or Google Vertex AI inside your own cloud account, confirming model availability in your region first.
Task-based evaluation
Test suites built from your real historical cases, so we measure whether the agent is right often enough to trust before anyone relies on it in production.
Bounded, logged actions
Every action an agent can take is defined and limited, with full logging, so a person can see exactly what it did and step in where judgement is needed.
Where this reader is stuck
You have read that AI agents can do real work, and you have watched a demo that looked impressive. The gap is knowing what is actually safe to put in front of customers, and which model to build it on. Meanwhile your staff still re-key data between systems, answer the same questions every day, and read long documents to pull out a handful of facts. The instinct is to pick whichever model is in the news and switch it on. A fortnight later it is either giving confidently wrong answers about your business or sitting unused because nobody trusts it.
Why Claude on its own does not finish the job
Claude is a strong model. It follows instructions closely, uses tools in a sensible order, and reasons across long documents without losing the thread. None of that, by itself, makes an agent that earns its keep. Three things have to be added, and none of them come with the model.
The first is your data. An agent that answers “what is our refund policy on a sale item” is only useful if it reads your actual policy, not a plausible average of every policy on the web. We ground agents in your real information with retrieval over your knowledge bases and integrations into the systems where the answers live, so the agent cites your material with the source attached. This is principle #5, AI-accessible internal data, applied directly. You can read more in our approach.
The second is traceability. When an agent gives a wrong answer, you need to know why and change it safely. We keep the prompts, the tools the agent can call, and the design choices behind it under version control, the same way we manage code. Every change is recorded and reversible. That is principle #6, version-controlled prompts and decisions, and it is what gives you an audit trail when the work touches customers or regulated data.
The third is purpose. We do not start with what Claude can do. We start with a task that costs your team hours, then decide whether an agent is the right fix at all. That is principle #8, a focus on the result rather than the novelty. If a simpler automation would do the job, we say so and build that instead.
How we deliver it on Claude
We work in small, reviewable batches, not one big switch-on, so risk stays low and you see value early.
We pick one repetitive, high-volume task where a wrong answer is recoverable, and agree what good looks like first. We connect the agent to the right documents and systems so its answers come from your business. We keep a person approving anything that matters until you trust the agent. We choose the smallest Claude model that does the job well rather than the largest by habit, then set cost and rate limits so spend stays predictable. We test the agent on your real past cases, measure where it is right and wrong, release to a small group, and expand once the numbers hold.
If your data rules require Claude to stay inside your own tenancy, we confirm early which models are available through Amazon Bedrock or Google Vertex AI in the regions that meet your residency obligations, and design for that from the start.
When Claude is the right call, and when it is overkill
Claude is a strong choice when an agent has to use tools reliably and reason across long or complex documents. It is the wrong call when a task is simple enough that a smaller, cheaper model would handle it just as well, when you need a model family Anthropic does not offer, or when another provider genuinely fits a specific job better. We are vendor-neutral. We treat the model as a means to your outcome, not a default, and we will recommend against Claude when something else serves you better. Humans stay in control of decisions throughout. The agent does the admin and surfaces the result, and a person makes the final call.
Related
Explore the wider service in AI Agents, and compare model options across Foundation Models. See how this applies in FinTech & Banking, Healthcare and Professional Services.
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Frequently asked.
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See what a Claude agent would do for one workflow
Pick the repetitive task that eats the most of your team's week. Tell us how it works today and we will show you whether Claude is the right fit, and what the agent would look like.
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