What differentiates an agent from a simple LLM function?

Agents incorporate stateful planning, tool orchestration, feedback loops, and policy-aware execution beyond single-turn completion.

How do you prevent runaway tool invocation?

Token & tool budget guards, explicit reasoning chain signature checks, and confidence-threshold halts with audit logging.

Which KPIs matter for agents?

Task success rate, intervention rate, mean tool depth, latency distribution, hallucination exception frequency.

Enterprise AI Agent Architecture & Governance

We design resilient AI agent systems emphasizing deterministic guardrails, incremental autonomy, and measurable outcome attribution. Our approach avoids agent sprawl by aligning each agent pattern to a crisp, auditable objective with bounded tool surface area.

Reference Pattern Layers

Intent Classification: Route tasks to agent archetypes.
Planner: Constraint-aware step synthesis with token budgeting.
Tool Executor: Structured schema invocation with retry & circuit breaking.
Memory & Context: Ephemeral chain memory + durable learning ledger for reuse.
Oversight: Policy & safety checks gating action / output surfaces.
Telemetry: Traces, tool latency, decision tree shape metrics.

Evaluation & Drift Control

Scenario suites exercise multi-step reasoning branches, capturing delta success distribution over time. We apply action variance diffing to detect emergent tool misuse or regression after model upgrades.

Observability Essentials

Structured reasoning traces & tool invocation spans
Autonomy escalation % (human intervention rate)
Hallucination exception taxonomy tracking
Cost per successful task (token + infra)
Latency by plan depth percentile

Governance Controls

Policy-as-code guardrails, capability allowlists, red team scenario injection, signed tool manifest integrity checks, and audit replay harnesses ensure operational trust.