TBW / Agentic AI
Agentic AI with boundaries built in
An AI agent can interpret a goal, choose from permitted steps and use tools to work toward an outcome. TBW’s proposed architectures place identity, permissions, human review and traceable execution around that process.
This is an architectural service overview—not a live agent, customer deployment, or promise of unsupervised autonomy.
Chatbot versus agent
Chatbot
Primarily responds to a message; it may retrieve knowledge but need not take action.
Single agent
Plans within a defined task and calls allowlisted tools, subject to step limits and authorization.
Multi-agent system
An orchestrator delegates bounded subtasks to specialists; each has a distinct role and permission scope.
Neither label guarantees reliability or autonomy. Outputs and retrieved content remain untrusted until checked.
Reference architecture: request to audit
Conceptual sequence, not an active integration on this page. The orchestrator routes work to specialists; policy determines what tools and data each may reach.
- Request
- Plan
- Specialist
- Tool / RAG
- Approval
- Execution
- Audit log
On a narrow screen the flow becomes a numbered reading sequence. Motion highlights one stage at a time and stops when reduced motion is preferred.
Specialists, context and connectors
Planning and research
A planning role decomposes a request. A research role gathers permitted evidence. The orchestrator checks handoffs and max steps rather than giving each role unrestricted access.
Memory and RAG
Short-lived task state helps a workflow continue; persistent memory needs retention and access rules. RAG retrieves approved source passages to ground answers, but citations and source quality still need review.
MCP and tool calling
Model Context Protocol can expose structured tools and resources; it is not an authorization system. Validate tool schemas, credentials, user identity, allowlists and arguments server-side. MCP & API integration →
n8n automation
Where suitable, n8n can run an approved workflow after an agent proposes a bounded trigger. Keep administration private, restrict webhooks and credentials, and log execution. No n8n connection is claimed here.
Human approval
Show the proposed change, target system and consequences before a sensitive write. A human may approve, reject or request revision; silence is not approval.
Audit and recovery
Record actor, tool, approval decision, timestamps and outcome with appropriate data minimization. On denial, timeout, provider failure or exhausted steps, stop safely and hand off.
A bounded execution envelope
Assign a separate agent identity and least-privilege authorization for each connector. Permit only named tools and destinations, enforce per-call timeouts and a maximum step budget, and require approval before sensitive writes.
Tool output, documents, web pages and model text are untrusted. Prompt injection must not change permissions. Failed checks halt or escalate; they do not silently retry with broader access.
Illustrative enterprise workflows
Knowledge-assisted triage
Retrieve approved documents, summarize with source links, and pass an uncertain result to an owner—no automatic record write. RAG architecture →
Service request preparation
Draft a ticket update from approved context, present the exact change for human approval, then use a scoped API identity if authorized.
Workflow handoff
Route a validated request into an allowlisted automation with a clear failure state. Integration approaches →
These are design examples, not customer case studies or presently available public-site actions.
Explore the existing agentic AI routes
Agentic AI Automation · AI Agents & Workflow Automation · Agent Design & Orchestration · Controlled Tool Access · MCP & API Integration