Multi-Agent Friction
Why it matters
Without centralised strategic infrastructure, different agents pursue different sub-goals. A growth agent may try to spend the entire budget while a risk agent tries to freeze it. A logistics agent optimises for speed by booking air freight while a sustainability agent cancels it to meet carbon goals — resulting in delayed shipments and double the work. This friction leads to high token costs but zero business progress, creating a deadlock that only a shared strategic layer can resolve.
How Stratafy addresses this
When multiple AI agents pursue conflicting objectives because they lack shared strategic context — a sales agent promising features the product team hasn't prioritised, or a cost-cutting agent undermining a growth strategy. Stratafy provides centralised strategic infrastructure via MCP so all agents share the same priorities, guardrails, and context.
Centralised strategic infrastructure via MCP
All AI agents query the same live strategic layer through Model Context Protocol. A marketing agent and a product agent see the same priorities, the same risk boundaries, and the same strategic constraints — eliminating the conflicting objectives that emerge when agents operate from different context sources.
Role-aware agent context generation
Configurable agent profiles generate strategic context tailored to each agent's role. A marketing agent gets brand values and positioning; a product agent gets roadmap priorities and technical constraints. Each agent sees the full picture through its own lens, but all lenses share the same source of truth.
Semantic guardrails as shared boundaries
Foundation-level constraints — values, principles, risk tolerance — are queryable by every agent as operational boundaries. When any agent's proposed action conflicts with a stated value or exceeds defined risk boundaries, the system flags it before execution — regardless of which agent initiated it.
Audit trail across all agent activity
Agent token logging tracks every MCP tool invocation across all connected agents. When agents produce conflicting outputs, the audit trail reveals what context each agent had, what tools it called, and where the divergence originated — making multi-agent coordination debuggable rather than opaque.