Agent Workload Assurance Execution Risk from Silicon to Semantic
Before an autonomous agent executes a critical task, someone needs to answer: "Is it safe to act right now?" FFWD is the only platform that answers this quantitatively — with a real-time Execution Risk Score that evaluates the agent, the full stack beneath it, and the task at hand.
The 2-way problem
The stack affects the agent. The agent affects the stack.
Most agent safety solutions watch one direction — what the agent says and does. FFWD watches both.
Stack » Agent : Infrastructure instability silently degrades agent performance. A GPU memory leak, a saturated network path, a database under contention — none of these trigger agent-level errors, but all of them affect the quality and safety of what the agent does next.
Agent » Stack: Agent actions ripple outward into downstream systems. A legitimate-looking query triggers a full table scan. A configuration change degrades adjacent services. An authentication chain escalates privileges nobody intended. The agent's logs show nothing wrong. The surrounding environment is where the evidence lives.
The clues and symptoms for agent anomalies are scattered across its surrounding environment — not just inside the agent itself.
WHAT THIS ISN’T . . .
This is not prompt injection defence. Not output filtering. Not chatbot guardrails.
Those solutions address real problems — for conversational AI. FFWD Agent Assurance addresses the agents that most safety solutions don't cover: infrastructure agents, data pipeline agents, deployment agents, identity management agents. The agents with elevated privileges, largely irreversible actions, and large blast radii.
Their failure mode isn't a harmful response. It's an operational cascade that shows up hours later in an outage report attributed to "infrastructure issues" — not to the agent that caused it.
EXECUTION RISK SCORE
A composite Go/No-Go verdict before every critical action.
Agent State
Model interaction health, behavioral drift, anomaly patterns in the agent's own telemetry
Stack State
Infrastructure health from silicon to containers, evaluated by FFWD's cross-domain anomaly correlation across the full stack the agent depends on and acts upon
Task Criticality
The impact level of the specific task the agent is about to execute. A routine log query and a production network re-route carry different risk thresholds.
The score is a composite of quantitative marker signals from FFWD's eight ML models and qualitative assessment from LLM reasoning (Claude, GPT, Gemini, Grok, or on-prem models). Not purely statistical. Not purely generative. Both.
AGENT TELEMETRY COLLECTION
Zero-instrumentation. No code changes. Deploy alongside your agents.
FFWD collects agent telemetry through three approaches — all non-intrusive:
eBPF Probe — Generates OTEL traces from agent activity at the kernel level and sends to FFWD's anomaly backend. Captures main agent and all sub-agents.
Rust Collector — Installed on the host machine for high-performance capture of agent interactions.
Sniffing Approach — Non-intrusive network probe. No agent code changes required.
Data collected spans four categories:
Identity — LLM provider, model, request type, agent name. Content — Prompts, responses, tool calls, function names. Usage & Cost — Prompt tokens, completion tokens, total tokens, reasoning tokens, cost, latency. Behavior — Error rate, retry count, finish reason, execution path.
Agent behavior drifts over time — model updates, context changes, infrastructure shifts. Continuous monitoring detects drift before it manifests as failure.
MCP-NATIVE DELIVERY
Risk Scores delivered where agents already work
The Execution Risk Score and Confidence Score are exposed via FFWD's native MCP server. Any MCP-compatible agent — Claude, Copilot, GPT, or custom-built — queries FFWD as part of its standard tool-calling workflow.
Enterprise-grade ReBAC permissions ensure multi-tenant, tiered access control. Agents only see the resources and scores they're authorised to access.