AI, Data & Intelligence
AI Agents
AI agents that complete defined tasks by calling your systems, such as updating a CRM record, triaging a ticket or reconciling a payment, with strict permissions, audit logs and human approval where it matters.
Capability overview
What ai agents involves
An AI agent does more than answer questions: it decides which tool to use, calls an API, reads the result and takes the next step until a task is complete. That makes agents useful for repetitive work spread across systems, and it also means a mistake can change real data, so design discipline matters.
We build agents for narrow, well-defined tasks first, with a small set of tools, read-only access where possible and explicit approval before actions such as sending emails, issuing refunds or changing records. Tool access increasingly uses the Model Context Protocol, and every step is logged for review.
What is included
What goes into a reliable agent
Task and tool design
The task broken into steps, and each system action exposed as a narrowly scoped tool with validated inputs rather than broad API access.
Permission boundaries
Agents act with their own service identity and least privilege, never with a shared administrator account.
Human approval points
Consequential or irreversible actions paused for a person to approve, with the agent's reasoning and proposed change shown.
Audit and replay
Every model call, tool invocation and result recorded so behaviour can be reviewed and failures reproduced.
Task evaluation
A suite of realistic scenarios, including tricky and adversarial ones, measuring completion rate and error types before release.
How we work
How we deliver ai agents
Task selection
Candidate tasks chosen where steps are repetitive, rules are clear and the cost of an error is containable.
Manual walkthrough
The task performed by hand with the people who do it today to capture edge cases and implicit rules.
Agent build
Tools, prompts and control logic built and tested in a sandbox connected to non-production systems.
Shadow mode
The agent proposes actions alongside human operators without executing them, and its accuracy is compared.
Supervised launch
Execution enabled for a share of cases with approvals, then autonomy widened as measured accuracy supports it.
Related capabilities
Related capabilities in Artificial Intelligence
Agentic AI Systems
Multi-agent and long-running AI workflows that plan, delegate and coordinate across several systems and teams, with orchestration, shared state, checkpoints and governance designed for production.
Conversational AI
Customer-facing chat and voice assistants on your website, app, WhatsApp and phone lines that resolve common requests, speak your customers' languages and hand over to people with full context.
Enterprise AI Integration
Connecting AI capabilities into the systems your organisation already runs, such as ERP, CRM, ITSM, document management and collaboration tools, with identity, permissions and audit carried through.
AI Model Integration
Wiring trained machine learning models and model APIs into existing software, from real-time scoring endpoints and batch predictions to model versioning, fallbacks and safe upgrades.
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Where this applies
Healthcare & Life Sciences
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Banking, Financial Services & Insurance
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Questions & answers
Questions about AI Agents
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionAn agent handles a single task with a focused set of tools. An agentic system coordinates several agents or long-running workflows with planning, shared state and hand-offs, which is considerably more complex to build and govern.
Sometimes, through database views, file exchanges or robotic process automation as a bridge. Browser automation by the agent is possible but less reliable, so we prefer a proper integration layer where feasible.
Content the agent reads, such as emails or web pages, is treated as untrusted. Tools are scoped tightly, sensitive actions need approval and injection tests are part of the evaluation suite.
A single-task agent with two to five tools typically reaches supervised production in six to ten weeks, including shadow mode testing.
Next step
Discuss ai agents with Acmez
Share what you need to change, build, integrate or support. We will map the practical next step.