AI Agents & Automation
Put AI to work inside real business processes.
AAKoryx designs and prototypes AI agents that work with business systems, data and workflows — helping organizations validate a practical use case before committing to a larger AI initiative.
The starting point
One bounded business problem, and a clear definition of what a useful outcome looks like.
Operational AI
AI demos are easy. Operational AI is harder.
An agent working inside a real business process needs more than a model. We approach agents as operational systems, not chatbot demonstrations.
- A defined job
- One responsibility, with a clear outcome.
- Reliable business context
- Current, accurate information from the right sources.
- Controlled system access
- Only the systems and data the job requires.
- Permissions
- Explicit limits on what it may read and change.
- Human approval points
- People confirm the decisions that matter.
- Escalation paths
- A clear route to a person when a case falls outside its job.
- Observability
- A record of what it did, and why.
- Failure boundaries
- Defined behavior when it is uncertain or something fails.
Example agents
The kinds of agents a proof of concept can explore.
Examples, not packaged products: each agent is scoped to your process, your systems and your rules.
Operations agent
Monitor operational information, identify exceptions and surface actions requiring attention.
Customer service agent
Answer questions using approved business knowledge and route or escalate requests when human intervention is needed.
Sales agent
Prepare account context, research information and assist with controlled sales workflows.
Document agent
Read business documents, extract relevant information and initiate controlled downstream workflows.
ERP / Odoo agent
Retrieve operational context from ERP data and perform explicitly authorized actions through supported interfaces.
Internal knowledge agent
Help employees find answers from governed internal knowledge sources.
Guardrails
Useful agents need boundaries.
Before an agent moves toward production, its limits are designed as deliberately as its abilities.
- What information may the agent access?
- Which systems may it use?
- What actions may it take?
- When is human approval required?
- How are its actions recorded?
- How are exceptions handled?
- What happens when it is uncertain or fails?
These boundaries are defined with your business, IT and security owners, and depend on your systems and policies.
System connection
An agent becomes useful when it is connected to the business.
The model is one part. Business context, the right tools and clear guardrails turn it into an agent that can do a job.
Model
Reasoning and language
Business context
Your data, documents and rules
Tools and systems
The applications it is allowed to use
Guardrails
Permissions, approvals and escalation
Result
Operational agent
A defined job, done inside your process
Systems an agent may connect to
- ERP / Odoo
- CRM
- Business applications
- Documents
- Knowledge repositories
- APIs
- Approved external services
Have an agent use case in mind?
Start with a bounded business problem and determine whether an AI agent can produce a useful operational outcome.