AI Operations · Custom Agent
Build an AI agent around real workflows, real data, and real control points.
A custom AI agent is a software project with AI inside, not a chatbot installation. Golden Sea starts with one workflow, defines data and permissions, connects tools, and implements logs and human escape routes.
Send a problem to diagnosePermission boundary
Read approved sources only. Actions require approval, with fallback and escalation.
A fit when
- High-volume workflows that require context.
- Source data that can be normalized and permissioned.
- A business owner can own rules and exceptions.
Not a fit when
- The process changes daily and has no owner.
- Only a standalone off-the-shelf agent is needed.
- AI is expected to make high-impact decisions autonomously.
Where the workflow breaks
Staff search too many sources for one answer.
Rigid automation cannot handle contextual work.
AI pilots lack logs explaining failures.
Current flow
- 01Receive a request
- 02Search documents manually
- 03Move across multiple tools
- 04Lose decision rationale
Proposed flow
- 01Classify request and permissions
- 02Retrieve relevant approved sources
- 03Suggest or execute rule-bound tool actions
- 04Log and escalate when uncertain
AI handles
- Interpret requests, retrieve data, and draft recommendations.
- Call tools within granted permissions.
- Summarize logs and identify out-of-rule cases.
People approve
- Rules, data sources, and tool permissions.
- Actions with financial, legal, or brand impact.
- Exceptions and process changes.
Required inputs
- Process map, owner, SLA, and exceptions.
- Knowledge base and system permissions.
- Accepted input/output test cases.
Deliverables
- Architecture, data map, and threat/permission model.
- Agent workflow, integrations, and admin controls.
- Test set, logs, runbook, and handover.
Scope and limits
- A simple OpenClaw setup is not the core product.
- The model receives no broader permissions than the task requires.
- Quality depends directly on source data and test coverage.
Implementation timeline
Weeks 1–2
Discovery, process/data audit, and prototype boundary.
Weeks 3–5
Build workflow, RAG, tools, and logging.
Week 6+
Pilot, red-team, handover, and log-driven tuning.
Permitted proof
Golden Sea's portfolio includes AI, chatbot, and recognition projects. Each proposal uses only relevant real work; a demo is not presented as company-wide automation proof.
Security & governance
- Least privilege for tools and data.
- Separate test/production environments and secrets.
- Mandatory audit logs, fallback, and human escalation.
Frequently asked questions
How is this different from a chatbot?
A chatbot primarily converses; a custom agent can retrieve data, use tools, and run bounded workflow steps.
Does every agent need RAG?
No. RAG is used when private-source retrieval is needed; stable tasks may be better served by rule-based workflows.
Read before implementation
Start with one clearly bounded problem.
Send the current process, source data, and the point where work slows down. Golden Sea will return a diagnostic scope before proposing a build.