Agentic AI and automation

SDS / EXPERTISE 02

Intelligent workflows that reduce coordination and keep work moving

We design intelligent workflows that take care of routine coordination across tools, data and teams, while keeping people involved where judgement, approval or accountability matters.

We help organisations move beyond isolated prompts and chat interfaces towards systems that can understand context, use approved tools, monitor progress, handle routine variation and escalate exceptions safely.

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01 /What agentic AI changes

From automating steps to coordinating outcomes

Traditional automation is highly effective when a process follows predictable rules. It can move data, create records, send notifications and trigger actions consistently.

Agentic AI becomes useful when the work requires a greater degree of interpretation and coordination. An agent can evaluate context, determine the next permitted action, interact with several systems, gather missing information and respond when the process does not follow the expected path.

The strongest solutions for complex organisations often combine both. Deterministic automation provides reliability for predictable actions, while agentic capabilities help manage context, variation and orchestration.

02 /Capabilities

What we can help you deliver.

Process discovery and redesign

Map the current workflow, identify unnecessary coordination and determine where automation would create the greatest value.

Agentic workflow design

Define agent responsibilities, permitted actions, decision boundaries, tools, data sources and human escalation points.

Integration with existing systems

Connect agents and workflows to the applications, APIs, databases and services already used by your organisation.

Knowledge and employee assistants

Create grounded assistants that help employees or customers find reliable information, complete tasks and reach a person when necessary.

Document and information workflows

Extract, validate, route and act upon information contained in documents, forms, messages and business systems.

Monitoring and exception management

Track workflow progress, detect failures or missing information and route exceptions to the appropriate owner.

Evaluation and continuous improvement

Assess quality, accuracy, task completion, exceptions, cost and user outcomes after deployment.

03 /Controls

Autonomy within defined guardrails

An AI agent used within an organisation should never have more access or authority than the process requires. We define the operating boundary before implementation and make important actions observable.

Role based and least privilege permissions
Read only and read and write separation
Approved tools and trusted actions
Human approval for sensitive or irreversible changes
Data access controls
Full action and decision logs
Confidence thresholds and validation rules
Timeouts, safe failure and recovery paths
Cost and usage limits
Ongoing performance monitoring

04 /Suitable processes

Where intelligent orchestration creates value

Employee lifecycle coordination
Customer and supplier onboarding
Service request fulfilment
Internal support and knowledge access
Finance and approval workflows
Compliance and document processing
Operational exception management
Sales and account administration
Engineering and platform workflows

05 /From assessment to working automation

From assessment to working automation

Automation consultancy

We examine how the work happens today, where coordination is consuming time and which decisions still need human judgement. From there, we identify realistic opportunities, define the right level of automation and set out the controls, architecture and delivery route needed to proceed safely.

Agentic system delivery

Our team can design, build and integrate the workflow within your existing environment. We account for access, permissions, approvals, exception handling, evaluation and monitoring from the outset, because reliable automation depends as much on how it operates as on what it can do.

INFORMED BY PRACTICE

Choose the level of autonomy the work needs.

Predictable steps often suit a defined workflow. Agents become useful when a task needs interpretation, tool selection or adaptation. More autonomy introduces trade-offs in cost, latency and reliability, so begin with the simplest approach that meets the requirement.

Controls need to extend across the lifecycle: identify the operating context and risks, agree who is accountable, evaluate performance and manage issues as evidence changes. A good demonstration is a starting point; it does not establish readiness for production.

Anthropic: Building effective agents ↗ NIST: AI risk management functions ↗

GOOD QUESTIONS

A few useful answers.

What is agentic AI?

Agentic AI describes systems that can interpret context, select permitted actions, use approved tools and coordinate work towards an outcome. Within a complex organisation, that autonomy should remain within explicit permissions and controls.

How is agentic AI different from traditional automation?

Traditional automation follows defined rules and is ideal for predictable steps. Agentic capabilities help when work involves context, variation or coordination across several systems. Strong solutions often combine both approaches.

Will an AI agent be allowed to make decisions without people?

Only where the process, risk and agreed operating model make that appropriate. Sensitive, consequential or irreversible actions can require human approval. Escalation paths are designed into the workflow.

Can agents work with our existing systems?

Yes, where the systems provide suitable APIs, interfaces or other approved integration routes. We assess access, data, security and reliability before defining the integration approach.

How are agent actions controlled?

Controls can include role based access, least privilege permissions, approved tools, trusted actions, validation rules, confidence thresholds, human approvals, logs, timeouts and cost limits.

What happens when the agent is uncertain or a process fails?

The workflow should fail safely. Depending on the use case, it can request information, retry a permitted action, stop, create an exception or escalate to an appropriate person with the available context.

How do you measure whether an agent is working?

Measures may include completion rate, accuracy, handling time, exception rate, human intervention, user outcomes, cost and the operational measure the process is intended to improve.

Can you automate a process without using agentic AI?

Yes. If the process is predictable, conventional workflow automation may be simpler, more reliable and easier to govern. We select the approach after understanding the work.

CONNECTED CAPABILITIES

AI strategy & implementation

Decide where AI is worthwhile, establish the right controls and take credible opportunities into use.

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Bespoke software development

Build or modernise web, mobile and internal applications around specific users, processes and requirements.