What this covers
- Agents connected to your real systems — ticketing, CRM, databases, internal APIs — rather than a chat window that can only talk
- Multi-step processes such as researching an enquiry, drafting the response, and preparing the record for approval
- Human-in-the-loop checkpoints, so anything that spends money, contacts a customer, or deletes data waits for a person
- Evaluation harnesses that replay real cases against every new version, so a change that quietly makes things worse gets caught
- Guardrails and scoped permissions, giving each agent access to exactly the tools its job requires and nothing more
- Full audit trails: what the agent decided, which tools it called, what it received, and what it did next
How we approach it
An agent is software that gets to choose its own next step, so the design question is not what it can do but what it must never do. We start there. Each agent gets its own credentials with the narrowest permissions that let it work, and destructive or costly actions require an approval it cannot grant itself.
Anything an agent reads from the outside world — a web page, an email, a document a customer uploaded — is treated as untrusted input, because text like that can be written specifically to redirect an agent. Instructions come from your configuration; retrieved content is only ever data. That separation is the difference between a useful agent and one that can be talked into acting against you.
We start narrow: one workflow, one clear success measure, running alongside your existing process rather than replacing it. When the evaluations show it holding up on real cases, the checkpoints loosen and the scope widens. Trust is earned from evidence, not granted at launch.
Tools we use for this
The platforms we reach for on this kind of work. The right choice depends on what you already run, and we will tell you when your existing tooling is the better answer.
- Anthropic
- OpenAI
- Python
- TypeScript
- FastAPI
- PostgreSQL
- Elasticsearch
- Redis
- Jupyter
- Docker
- Sentry
- LangChain
- LangGraph
- CrewAI
- Hugging Face
- Ollama
Common questions
How is this different from a chatbot?
A chatbot produces text. An agent takes actions — querying your database, updating a ticket, calling an API — and decides which to take next based on what it finds. That capability is genuinely useful, and it is exactly why the permission model matters so much more.
What stops an agent doing something damaging?
Three things working together: permissions narrow enough that damaging actions are not available to it, mandatory human approval for anything consequential, and a complete audit log so unexpected behaviour is visible immediately rather than discovered later.
How do you know it is working?
We build a test set from your real cases with a defined correct outcome for each, and every version runs against it. Accuracy becomes a number you can watch over time instead of a feeling, and a release that regresses gets caught before it reaches production.
Where does it make sense to start?
One repetitive multi-step process with a clear right answer and a person available to check the output. That is enough to prove the value and to reveal the edge cases that a wider rollout would otherwise hit all at once.
Talk to us about agentic ai
Tell us what you are trying to achieve and we will tell you honestly whether we are the right fit. We reply within 1 - 2 business days.
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