Multi-agent routing
A router that picks the right specialist for the request, with fallback and escalation paths. Not one god-agent that knows everything.
Multi-agent systems in production — routing, memory, tools, and cost governance.
We take agent prototypes into production: routing between specialised agents, shared memory and context stores, tool-use policies, deployment topology (edge vs. control-plane vs. LLM proxy), and cost governance for the whole graph — with tracing across agents to prove it.
A router that picks the right specialist for the request, with fallback and escalation paths. Not one god-agent that knows everything.
Context stores agents actually share correctly, with TTLs, isolation, and forgetting policies. Cross-agent memory without cross-agent leakage.
Per-agent allowlists, capability tokens, and sandboxed execution. Agents can't invoke tools they weren't granted.
Deploying a new agent version means A/B against the old one on shadow traffic first. Rollback is a config change.
Token, latency, and dollar budgets per orchestration. Runaway loops caught by the platform, not by the CFO.
One trace ID follows a request through every agent, tool, and retrieval — replayable, exportable, auditable.
The words that separate insiders from readers.
When five agents cooperate and produce a wrong answer, root-cause tooling is embryonic.
Per-orchestration cost you can plan against, not the sum of unpredictable model calls.
Delegation models designed for humans, applied to agents that act on humans' behalf.
TTLs, forgetting, and isolation across a graph of agents. Regulatory-shaped where personal data is involved.
Every hop is another model call. Getting under a second across four agents is real engineering.