Harden · Implementation
Multi-User Agent Hardening Sprint
Stabilise the AI system you already have. We fix the controls that matter first, working inside your team.
Length 4–8 weeks
Mode Implementation
Team Embedded with yours
You end with A hardened system
In
Every request
01 Models
02 Prompts
03 Guardrails
04 Budgets
05 Tools
06 Traces
07 Evals
What this sprint switches onFilled squares = layers covered
What we fix
Before → After 01 Model control Model access scattered across services Model access and config centralised
02 Prompt operations Prompts and configs that are hard to change safely Prompts and configs versioned and safe to change
03 Guardrails Weak or inconsistent input and output guardrails Pre and post guardrails around key flows
04 Budget governance Cost exposure from loops, retries or fan-out Budget controls and containment paths
05 Tool governance Tool and MCP permissions that are too broad Tool auth, scopes and permissions tightened
06 Observability Missing traces, dashboards and alerting Traces, dashboards and alerts in place
07 Evals Thin or nonexistent eval coverage Release evals and regression checks running
When it fits
You already have something running. The problem is that it’s fragile, hard to trace, or expensive to operate. We take the riskiest gaps first, so the system gets cheaper to run, easier to debug and more reliable under load.
Customer-facing beta or production systems
Internal systems now shared across teams
AI products with incident history or rising spend
Teams that want focused execution over open-ended advice
What you walk away with
01 Hardened architecture with the fixes implemented
02 Updated scorecard and risk register
03 Dashboards, alerts and trace coverage
04 Eval harness and release checklist
05 Operating notes for your team