Automation
For explicit, repeatable rules and actions with predictable exceptions.
AGENTIC AI
AI agents for processes where multiple steps, systems and decisions come together.
An agent becomes relevant when one isolated AI action is not enough: information needs to be gathered and interpreted, decisions made within boundaries, and existing systems need to move the process forward. Link2Leap designs these workflows from the process outward, not from the AI tool.
When an agent becomes relevant
When the real work happens between systems and decision points
Agentic AI is most relevant to workflows where context, exceptions and follow-up actions cannot be handled by one fixed rule or one application.
- 01
A process spans several systems and still depends on people to coordinate the work between them.
- 02
People repeatedly gather, interpret and compare information before they can take the next action.
- 03
Decisions depend on context, exceptions and several consecutive steps.
- 04
Work gets stuck in handovers and queues because no single system owns the complete workflow.
- 05
An existing AI feature provides an answer, but the real value comes from safely carrying the process forward.
First, what each step needs
Let the workflow determine how much intelligence and autonomy it needs
We first examine where the process is predictable, where interpretation is required and where an agent needs to coordinate several steps. Only then do we choose automation, AI integration or an agent for each step.
Many workflows combine these routes. An agent then handles only the steps that need coordination across systems and decisions.
AI Integration
For one bounded step requiring interpretation, classification, extraction, generation or decision support.
Agentic AI
For multiple steps, systems and decisions that require coordination within defined permissions and boundaries.
What we build
Controlled workflows that move the process forward
We combine AI, software logic and system integration into one workable route, with explicit points for human review and exceptions.
Agentic Workflows
Multi-step workflows that gather information, reason within constraints and move the process forward deliberately.
AI Orchestration
Organise AI capabilities, deterministic logic, system actions and recovery paths as one controlled workflow.
Human-in-the-loop Automation
Design clear approval, review and escalation points where human judgement remains necessary.
How an agentic workflow works
From trigger to controlled action
The exact steps vary by process. A useful workflow always makes clear which context is used, where a decision is made and what subsequently happens across systems.
Not every workflow uses every step in the same way. Design follows the process and the risk of each action.
- 01
Trigger / input
An event, request or change starts the workflow.
- 02
Gather context
The workflow retrieves permitted information from relevant sources and systems.
- 03
Evaluate / reason
AI and software logic assess information, rules, context and exceptions.
- 04
Decide within boundaries
The next step is selected within explicit permissions, conditions and risk limits.
- 05
Act through systems
The workflow performs permitted actions through applications, APIs or other operational interfaces.
- 06
Validate / escalate
The outcome is checked; exceptions move to recovery, fallback or human review.
- 07
Log / observe
Decisions, actions, errors and exceptions remain visible and traceable.
Controls before autonomy
Autonomy is not an on-off switch
The freedom an agent receives should be designed per action. A low-risk action may run automatically, while a decision with greater consequences requires explicit approval.
Greater autonomy only makes sense when reliability, consequences and recovery options allow it.
- Permissions & access
- Limit which data, functions and systems each step may use.
- Guardrails & boundaries
- Technically enforce which actions, values, situations or outcomes are and are not allowed.
- Human approval
- Place review or authorisation where consequences, uncertainty or policy require it.
- Validation & fallback
- Check inputs and outcomes and define a safe route when quality or availability falls short.
- Logging & observability
- Make behaviour, decisions, used context, errors and performance traceable.
- Exception handling
- Prevent unexpected situations from stalling silently or continuing without control.
Where value tends to appear
Process patterns worth investigating
These are typical workflow patterns, not claims about existing client cases. Suitability always depends on the process, data, systems and risk.
- 01Triage and enrich information before routing it to the right next step.
- 02Conduct research or preparation across multiple permitted information sources.
- 03Interpret documents and coordinate the process steps that follow.
- 04Support internal operational coordination across teams and systems.
- 05Investigate exception-driven workflows and escalate them deliberately.
- 06Prepare and handle recurring handovers in knowledge work.
Within Link2Leap
A specialism within AI & Automation
Agentic AI is one possible route within AI & Automation. When the workflow depends on existing software, architecture, integrations or modernisation, our Software Engineering or Software Modernisation expertise can join, only when needed.
Explore AI & AutomationWhere we are less likely to fit
An agent needs to solve an operational problem
We are usually not the best fit when:
- The primary goal is simply to launch an AI chatbot or demonstration.
- Deterministic automation can already solve the process reliably.
- There is no stable process, usable data or access to the required systems.
- The organisation expects unrestricted autonomy without appropriate boundaries and control.
- The request is primarily about content marketing or generative media rather than operational process improvement.
A process that may need more than conventional automation?
Tell us about the workflow, systems, decisions and bottlenecks. We can examine whether Agentic AI is appropriate and what level of autonomy makes sense.
Discuss whether an AI agent fits