Knowledge Assistant
Find and summarise authorised information with source references.
04 / AI AGENT DEVELOPMENT
AI agent development for real business work
Agents that connect knowledge, tools and business rules—designed with evaluation, permissions, human approval and fallback from the start.
Discuss an AI agentWHO IT IS FOR
For teams working across multiple information sources or multi-step tasks that a question-answer chatbot cannot complete.
OUR APPROACH
Define the outcome, scope and acceptance criteria before choosing technology, so the investment solves a real problem and remains maintainable.
PROBLEM
Problems to solve before adding another tool.
- Knowledge is scattered across documents and systems
- Teams repeat the same research and summaries
- Rigid automation cannot handle nuanced cases
- Accuracy, access and sensitive data require control
OUTCOME
What the project should make better.
- A use case with measurable success criteria
- An agent connected to only the knowledge and tools it needs
- Human approval, fallback and audit history
- Evaluation and monitoring before broader autonomy
USE CASES
Useful scopes shaped around the work.
Sales Assistant
Qualify needs, prepare context and hand the opportunity to a person.
Document Processing
Extract, classify and route documents for human verification.
Research & Operations Agent
Gather across systems and prepare a governed action.
SYSTEM THINKING
An agent architecture designed for useful control
Connections, permissions and failure states are designed alongside the interface, so a simple front end does not hide operational risk.
Inputs
- Event / request
- Business context
- Knowledge
System & outcomes
- Tools & APIs
- Human approval
- Audit log
DELIVERABLES
Deliverables that can be accepted and operated.
Use-case workshop and risk assessment
Knowledge, tool and permission architecture
Prototype with real workflow samples
Evaluation, guardrails and human handoff
Deployment, logging and improvement plan
SCOPE & INVESTMENT
Scoped around the work—not the service label.
Investment includes solution design and development, integrations and recurring model or platform usage. Data quality, risk and evaluation needs usually matter more than the number of screens.
- Data volume and quality
- Tools, APIs and permissions
- Accuracy and evaluation criteria
- Model usage, monitoring and support
FAQ
Questions before the work begins.
How is an AI agent different from a chatbot?
A chatbot usually responds. An agent can use data, call tools and continue through several steps, which requires stronger permissions, approval and monitoring.
Can an agent connect to LINE or company systems?
Yes when those systems expose a suitable API or integration path. We review LINE OA, CRM, Google Workspace, databases and internal systems before defining the workflow.
How do you reduce incorrect answers?
We constrain sources and tools, validate outputs, build evaluation sets, log activity and escalate when confidence or policy requires a person. No approach guarantees 100% accuracy.
EXPLORE NEXT
Useful context for choosing the right scope.
Have a problem worth scoping clearly?
Share the context, outcome and systems involved. We will help identify the questions to answer before investing.
Start the conversation