Can an agent work in our existing systems without replacing them?

Yes — that is the point. Agents use your systems through their APIs (or, as a last resort, through controlled browser automation). Nothing is replaced; the agent becomes another, tightly permissioned user.

What stops the agent from doing something harmful?

Limits enforced in code, not in the prompt: narrow tool scopes, caps, allow-lists, approvals for irreversible actions, and step and cost budgets. Prompts guide behaviour; the system enforces boundaries.

How do we know it works before it touches real data?

Agents are tested against a library of scenarios in a sandbox, and rolled out in stages — usually starting with drafts that people approve, so you see its real accuracy before granting more autonomy.

Will agents replace our team?

Agents typically remove the repetitive part of a job — looking things up, copying data, drafting standard messages — and leave people with the decisions and exceptions. Plan for a changed workflow, not an empty desk.

What does an agent cost to run?

It depends on steps per task, model choice and volume. We model cost per task during the proof of concept; our LLM cost calculator gives a first orientation.

Service · Agentic AI

AI agents that do real work. With a kill switch.

An agent is an LLM that can use tools: search a database, update a CRM record, send an email, open a ticket. That makes agents far more useful than chatbots — and far riskier. We build agents that act within explicit limits, ask a human before anything irreversible, and leave an audit trail you can actually read.

The agent loops through plan → act → observe inside hard limits; risky actions route to a person.

What agents are good at today

Agents work best on tasks that are multi-step, rule-guided and tolerant of review: a human used to do them by looking things up in several systems, applying judgement, and taking one of a few possible actions. They are still a poor fit for open-ended goals with no clear finish line, or for actions where a single mistake is catastrophic and can’t be reviewed.

Support triage & resolution

Classify, gather order and account context, propose or execute standard resolutions within limits.

Back-office operations

Reconcile invoices against orders, chase missing documents, update records across systems.

Sales operations

Enrich leads, update the CRM after calls, draft follow-ups for a rep to approve.

IT & internal requests

Access requests, password resets, provisioning — with approvals and logs.

Research & reporting

Collect data from internal sources, compile a draft report, cite every number.

Data quality

Find duplicates, inconsistencies and missing fields; propose fixes for review.

Choose the level of autonomy deliberately

We usually start at level 2 and move up only when evaluation and production data justify it.
Level	What the agent does	Good for
1 · Assistant	Suggests the next step; a person does everything	New processes, high-risk domains, first rollout
2 · Drafts	Prepares actions (emails, updates) for one-click approval	Most business workflows — the sweet spot
3 · Acts within limits	Executes low-risk actions itself, escalates the rest	High-volume, well-understood tasks with clear limits
4 · Autonomous	Runs end-to-end, humans review samples	Rarely justified; only with strong evaluation history

The safety model

01Least-privilege tools

Each tool has a narrow purpose and its own credentials scope. “Run any SQL” is never a tool.

02Hard limits in code

Refund caps, rate limits, allowed recipients and step budgets are enforced by the system, not by the prompt.

03Human approval for irreversible actions

Payments, deletions and external communication wait for a person until you decide otherwise.

04Prompt-injection defence

Content from emails, web pages and documents is treated as data, never as instructions; sensitive tools are gated.

05Full audit trail

Every thought, tool call, input and output is logged with a correlation ID you can search.

06Cost and loop budgets

Maximum steps, tokens and time per task; runaway loops are stopped and reported.

How we test agents

1
Scenario library
We write realistic tasks with the expected end state: which records should change, which shouldn’t, what message should go out.
2
Sandboxed tools
Agents run against test copies or mocks of your systems, so scenarios can be replayed safely on every change.
3
Outcome checks
We score the final state, not the wording: correct action taken, nothing extra touched, limits respected.
4
Trajectory review
For failures, we inspect the step-by-step trace to decide whether the fix belongs in tools, instructions or limits.

Stack and standards

We build agents with plain, testable code around the model’s tool-calling API, adding frameworks only where they earn their keep. For connecting agents to many internal systems we use the Model Context Protocol (MCP), which gives each system a standard, permissioned interface. Everything runs in your cloud, with secrets in your vault.

Frequently asked questions

GuideAI agents in production: what breaksLoops, runaway costs, wrong-record actions, prompt injection and silent failures: how AI agents break in production and the patterns that prevent it.GuideMCP explainedThe Model Context Protocol in plain language: hosts, clients and servers, when to build an MCP server for your systems, and how to secure it.ServiceAI integrationAdd AI to the systems you run: classification, summaries, drafts, extraction and routing inside your CRM, ERP and helpdesk — securely, in your cloud.Free toolAI automation ROI calculatorCalculate hours freed, net monthly saving and payback period for an AI automation — using your own task volumes, costs and a conservative automation share.Guide11 ways to cut your LLM billLLM cost optimisation: prompt caching, model routing, shorter context, better retrieval, batching and budgets — checked against evals so quality holds.ServiceRAG developmentRAG development: AI assistants that answer from your documents and data with citations, permissions and measured accuracy — built in your cloud.

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