Retrofitting AI Safety into Production Systems

Adding safety controls to a production AI agent is more complex than building them in from the start. The agent has existing behavior that users depend on. Any change risks breaking workflows, increasing latency, or creating false denials that frustrate users. The goal is to add safety without disrupting service.

Principle: Additive, Not Disruptive

Every change should be additive. Do not modify the agent's core behavior. Instead, add a layer that observes, evaluates, and (eventually) enforces constraints on the existing behavior.

Strategy 1: Proxy Insertion

Place Authensor between the agent and its tools as a proxy. The agent's code does not change. Instead, its tool endpoints are redirected to the Authensor gateway, which forwards permitted calls to the original tools.

This approach works when:

• Tools are accessed over HTTP or network protocols
• You can modify environment variables or configuration without code changes
• The agent does not embed tool logic directly

Strategy 2: SDK Wrapping

If the agent calls tools through an SDK or library, wrap the tool execution function with Authensor's SDK. The wrapper intercepts calls, evaluates them, and delegates to the original function.

This requires a code change, but it is a small one: replacing direct tool calls with wrapped versions. The underlying tool implementations remain unchanged.

Strategy 3: Sidecar Deployment

Deploy Authensor as a sidecar process alongside the agent. The sidecar receives copies of all tool calls (via event emission or log tailing), evaluates them against policies, and reports violations. Initially, the sidecar operates in observation mode. Once you are confident in the policy, switch it to enforcement mode.

Rollout Sequence

1. Deploy in observation mode. Log all actions without blocking anything. Establish baselines.

2. Enable content scanning. Start with high-confidence detections only (PII patterns, known prompt injection signatures). Monitor false positive rates.

3. Enable deny rules for clearly prohibited actions. Data deletion, credential access, external communications that are never appropriate.

4. Enable approval workflows for risky actions. Financial transactions, data exports, account modifications.

5. Tighten constraints incrementally. Add parameter restrictions, lower confidence thresholds, and expand the set of actions requiring approval.

At each stage, monitor for increased error rates, user complaints, and agent task completion rates. If any metric degrades, pause and investigate before proceeding.

The entire retrofitting process typically takes four to eight weeks, depending on the complexity of the agent and the number of tools it uses.