Running StrictLLM as a service, AI Agents

StrictLLM Agency Implementation, Multi-Agent Workflows & Governance

Learn to architect auditable multi-agent systems for clients using StrictLLM's persona orchestration, granular tool permissions, and MCP sandboxing. This course teaches agencies how to design specialized AI personas, enforce governance policies across workflows, and deliver governed AI solutions that maintain client control and compliance.

Open the decision record for StrictLLM

What does running StrictLLM for clients commit you to?

Published figures for this service. Blank fields are not published.

Monthly tool cost
Vendor subscription cost is not published in the supplied pricing_tiers data, confirm the entry-tier price directly with StrictLLM before quoting. Setup investment should be estimated from your own measured engineering hours for MCP configuration, Podman sandboxing, and persona design; no dollar figure is modeled here.
Time to first value
Setup complexity: medium; time_to_value: days (as published)
Payback
Not modeled, client price, labor, usage, overhead, and expected volume are not supplied in the Level 1 data
Guided implementation
16 hours

Is StrictLLM worth running as a client service?

StrictLLM supports a defensible managed service for agencies with technical staff who can configure MCP servers, Podman sandboxes, and per-persona permissions; the evidence confirms the capability set but not the vendor price, client price, or delivery margin, so those must be captured from live engagements before committing to scale.

An agency-fit judgement for reselling this service. It is separate from the tool description on the decision record.

Before you start

What has to be in place before the first client engagement.

Tools and subscriptions

  • StrictLLM desktop install on the desktop OS the client will use
  • LLM provider API keys or local model endpoints (OpenAI, Anthropic, OpenRouter, LM Studio, llama.cpp, or Ollama)
  • MCP server configuration (stdio or SSE transport) for the client's private data source
  • Podman runtime for sandboxing local MCP servers
  • Published StrictLLM tier subscription (plan names not supplied in pricing_tiers; verify tier at purchase)

People and inputs

  • Engineer comfortable with MCP configuration and Podman container networking
  • Internal test persona set to validate orchestrating multi-persona @mention workflows before client delivery
  • A defined permission policy matrix per persona (Always Allow / Always Ask / Always Deny) for the pilot client
  • A review process for approval flows and audit logs on Enterprise if the client requires compliance evidence

Lessons in this course

7 lessons on running StrictLLM for clients.

  1. 01Why StrictLLM's Permission Layer Beats Another Chat Subscription for Agency DeliveryStrategy
  2. 02StrictLLM Permission Tier LadderConcept
  3. 03StrictLLM Rule: Adopt for Auditable Internal Agent Workflows, Not Client-Facing RetainersEvaluation Rule
  4. 04StrictLLM: Buy vs Skip (Desktop Persona Orchestration for Client Work)Decision Framework
  5. 05Why Agencies Fail With StrictLLM: The Desktop-Only Retainer IllusionFailure Pattern
  6. 06StrictLLM Permissioned Persona Rollout (6-8 days)Implementation Blueprint
  7. 07StrictLLM Persona Permission Hardening (Delivery)Operating Procedure

Included with the course

7 working documents for delivering this service.

  • Persona Architecture Worksheet for Client Workflowsworksheet
  • Tool Permission Matrix (Always Allow/Ask/Deny Setup)template
  • MCP Server Configuration Checklist for Private Datachecklist
  • Multi-Persona Orchestration SOP with @Mention Chainssop
  • Governance Policy Enforcement Guide for Client Approval Flowsguide
  • LLM Provider Assignment & Model Comparison Templatetemplate
  • Offline Deployment & License Validation Checklistchecklist

Listed by name. These documents are not yet published as individual downloads.