~/censiq
Censiq
Evaluation infrastructure for AI agents and the people who work alongside them.
The Problem
Organizations are deploying AI agents into real workflows faster than they can rigorously answer a basic question: how do we actually know if this is working? General-purpose benchmarks measure capability in the abstract. They rarely test the parts of a specific role that matter most in production — recognizing when to escalate, handling incomplete information, staying inside a compliance boundary, or knowing when not to act.
Product Thesis
Evaluation has to be role-specific to be useful. Censiq's thesis is that defensible AI-agent deployment decisions require simulating the actual work a role involves, comparing agent performance against a human doing the same task, and capturing evidence precise enough to hold up when a deployment decision gets questioned later.
System Overview
Censiq is built around a simulation-and-evaluation loop rather than a static test set.
Role definition layer
Encodes what a role actually requires — inputs, decision points, escalation rules, and failure costs — as the basis for everything downstream.
Simulation environment
Runs agents against realistic, structured task scenarios rather than static prompts.
Comparison engine
Scores agent output against a human-performance reference and against the role's defined success criteria.
Evidence & reporting
Captures concrete failure examples and turns them into a report a non-technical stakeholder can act on.
Evaluation Lifecycle
define_role()
Define role-specific criteria
Start from the actual job, not a generic capability benchmark — its inputs, edge cases, and failure costs.
simulate_work()
Simulate real work
Build task environments that resemble production conditions, including ambiguity and incomplete information.
run_evaluation()
Test before deployment
Run agents against the simulated environment under the same conditions they'll face in production.
compare_performance()
Compare human vs. agent
Benchmark agent performance against how a human currently performs the same role.
capture_evidence()
Identify failure points
Capture concrete evidence of where and how the agent fails, not just an aggregate score.
recommend()
Translate to a decision
Turn technical evaluation results into a deploy / don't-deploy / deploy-with-guardrails recommendation.
Example Workflow
Illustrative, not a disclosed client engagement — a generic walkthrough of how the lifecycle applies in practice.
A team wants to deploy an agent to triage inbound support tickets. Instead of relying on a generic benchmark, the role is defined around the team's actual escalation rules and edge cases. The agent runs against a simulated queue of realistic tickets, including ambiguous and incomplete ones, and its routing decisions are compared against how a human triager currently handles the same queue. Failure cases — misrouted urgent tickets, incorrect escalations — are captured as concrete evidence. The output is a specific recommendation: deploy for a defined subset of ticket types, with a human-in-the-loop guardrail for the rest.
Founder Role
- Product direction and roadmap
- System architecture and implementation
- Evaluation methodology design
- Customer discovery and early engagements
Technologies
Current Status
Censiq is active and under continued development. If you're deploying AI agents and need a defensible way to evaluate them before or after launch, I'd like to hear about it.