Multi-agent systems built for production, not pilots.
We design resilient AI agent architectures with deterministic guardrails, bounded tool surface areas, and full observability. Each agent is aligned to a crisp, auditable objective before the first line of code is written.
Six pattern layers. One delivery standard.
Our reference architecture separates intent classification, planning, execution, memory, oversight, and telemetry into distinct, independently testable layers. No agent sprawl.
Intent Classification
Route incoming tasks to the correct agent archetype with deterministic rules and confidence thresholds before any tool is invoked.
Planner & Orchestrator
Constraint-aware step synthesis with token budgeting and explicit reasoning-chain signatures. Plans are auditable before execution.
Tool Executor
Structured schema invocation with retry, circuit breaking, and a bounded tool surface area tied to each agent objective.
Memory & Context
Ephemeral chain memory for in-flight tasks plus a durable learning ledger for cross-session pattern reuse.
Oversight & Safety
Policy-as-code guardrails, capability allowlists, red team scenario injection, and signed tool manifest integrity checks.
Telemetry & Evaluation
Structured reasoning traces, tool invocation spans, autonomy escalation rate, and cost-per-successful-task tracking.
Four phases. Written exit criteria at each gate.
Scope & Baseline
Define the agent objective with crisp, auditable boundaries. Capture pre-agent task baselines and KPI targets before any design work.
Architecture & Guardrails
Design the planning loop, tool surface, and safety layer. Every tool gets a schema and a circuit-breaker before integration.
Evaluation Harness
Scenario suites exercise multi-step reasoning branches. Action variance diffing detects emergent tool misuse after model upgrades.
Production & Operate
Deploy with observability dashboards active from day one. Quarterly drift reviews catch regression before it reaches users.