Custom AI Solutions
Build a focused AI agent around a real workflow, the information it may use, and the actions it may take. GTWY combines language models, knowledge retrieval, memory, tools, and safeguards so teams can move from a defined use case to a testable agent without assembling every layer alone.
Where a custom agent is a good fit
The strongest use cases have a clear audience, bounded source material, and an outcome that can be reviewed. An agent should assist a defined process rather than act as an unrestricted replacement for human judgment.
Customer support
Answer recurring product or policy questions from approved help content, collect the details needed for a ticket, and hand uncertain or sensitive cases to a person.
Sales guidance
Ask structured qualification questions, explain relevant offerings, and help a visitor choose a next step while keeping pricing, availability, and account decisions in their source systems.
Internal operations
Help colleagues find procedures, summarize supplied material, prepare repeatable drafts, or trigger approved tools for routine updates with explicit confirmation where needed.
Configure behavior, context, and boundaries
Configuration starts with the system prompt: the agent's role, priorities, tone, required steps, refusal behavior, and escalation rules. Model selection then reflects the task's reasoning, response-time, and cost needs instead of assuming one model is right for every job.
Tools
Give the agent only the actions its workflow needs, with clear inputs and outputs. Read-only lookup, drafting, and write actions can be separated so consequential changes require confirmation.
Knowledge
Ground answers in selected documents, pages, or connected sources. For document-heavy work, GTWY’s managed RAG option handles retrieval so the agent can use relevant context rather than rely only on model training.
Memory
Choose what belongs in the current conversation and what, if anything, should carry across sessions. Retention should match the use case and avoid storing details that are unnecessary for the next interaction.
Guardrails
Define out-of-scope requests, blocked topics, formatting rules, fallback responses, and human handoff conditions. Tool permissions and input validation add boundaries beyond prompt instructions alone.
Instructions
Use concrete examples and ordered steps to resolve ambiguity. State which source wins when information conflicts and require the agent to acknowledge when the available context does not support an answer.
Conversation design
Plan welcome messages, clarifying questions, progress feedback, and completion states. A useful agent makes its next action understandable and gives users a practical route to correct or escalate it.
Test before publishing
Build a review set from expected questions, incomplete requests, conflicting documents, off-topic prompts, and attempted tool misuse. Review answer accuracy, cited context, tool arguments, escalation behavior, and whether memory changes later responses appropriately. Publishing should follow acceptance checks with the people who own the workflow, followed by observation and revision as source content and requirements change.
Connect it to the right surface
Use the managed AI API when an application needs to send queries and receive agent responses. Use the managed chatbot for a website conversation layer, or connect approved business tools to support lookups and workflow actions. Teams can also start from agent templates and adapt the instructions, knowledge, and permissions to their own process.
Define your first agent workflow
Bring a use case, the sources it should trust, and the systems it may need to reach. We can map those requirements into a scoped configuration and testing plan.