Mirrors

Founded by Aryan Singhal in 2026

Route dynamic context into AI agents during conversations based on matching session triggers

Pricing model

Demo

Rating

★ People love it

Time to value

Moderate Setup (1-3 hours)

You can use Mirrors to turn insights from execution traces into real-time guidance for AI agents without redeploying code. The system monitors ongoing interactions and injects tailored instructions, tool schema modifications, or updated tool descriptions only when matching scenarios arise. Developers can test routing conditions against historic session logs to inspect prospective matches and misses before turning them on. After deploying a rule, teams can measure results by comparing conversations that received new context against control holdouts directly inside their analytics workflow.

What Mirrors does

Inspect failed conversation traces to identify where agents deviate from intended behaviorsWrite conditional context snippets to redirect agent decisions in specific situationsSimulate routing rules against historical logs to spot unintended pattern matchesInject tailored prompt guidance dynamically into running agent conversationsRestrict tool input schemas on the fly without updating backend service codeCompare task completion metrics between routed sessions and control groupsRoll back failing context configurations instantly without executing code deploymentsRoutes contextual guidance into active agent sessions when predefined trigger conditions matchModifies tool descriptions, input schemas, and tool results without code redeploymentEvaluates routing rules against stored trace histories before activating them in productionExecutes context routing locally without extra network hops or secondary LLM callsCompares agent sessions against holdout groups using custom outcome metricsApplies situational guardrails mid-conversation instead of inflating global system promptsSupports session rollbacks when newly injected instructions fail to deliver expected results