The next frontier of agent engineering

AI Agent Evals & Readiness Harness

From demo to production · 7 evaluation dimensions · 4 harness patterns · CI integration

#AIAgentEvals#ReadinessHarness#LLMEvals#AgentSLA#2026

Why Evals Are the Biggest Agent Bottleneck

In 2026, the largest barrier in AI agent projects is not model selection — it's "runs in demo, breaks in prod three days later." Industry data shows 68% of agent projects stall at the evaluation stage: without repeatable evals, no one will route real user traffic through the agent. Evals plus a readiness harness are the engineering foundation that turns an agent demo into a production system.

Seven Evaluation Dimensions

01 Correctness: is the output right, measured against ground truth or a human gold set
02 Safety: does it generate harmful, non-compliant, or out-of-scope content
03 Cost: is the average token / dollar cost per task within budget
04 Latency: does p50 / p99 meet your SLA
05 Tool-use: are tool calls accurate, arguments reasonable, no infinite loops
06 Hallucination: does it fabricate facts or cite wrong sources
07 Regression: did the new version drop score against the previous baseline

Four Readiness Harness Patterns

Sandbox mode

Run evals in an isolated environment for PR checks / nightly jobs, zero production impact

Shadow mode

New agent receives real traffic in parallel with the old agent but does not respond to users; diff outputs

Canary mode

New agent takes 1-5% of traffic, monitor all 7 dimensions, auto-rollback on threshold breach

Gated mode

Make the evals suite a mandatory CI gate, any regression blocks the merge

CI Integration

Wire your evals suite into GitHub Actions / GitLab CI: every PR runs 200-500 test cases against the main-branch baseline. Claude Code and Codex CLI both support an `--eval-mode` flag producing structured JSON, easy to plug into Grafana or Datadog.

Handling Eval Cost Explosions

Enterprise users often ask: "Running the evals suite costs a fortune every month — 1,000 cases × daily × 4 agent versions = 120K API calls a month." Two answers: (1) move to a branded API subscription service with unified billing and predictable budget — e.g. QCode.cc enterprise subscription includes a dedicated evals quota; (2) cache + sampling — full set nightly, high-priority subset on PR checks.

Relationship to Harness Engineering

Harness Engineering is the macro system design — constraints, feedback loops, lifecycle, context. Readiness Harness is the feedback-loop subset, focused on the single decision: "can this agent go to production?" They reinforce each other: a well-designed harness makes evals diagnose problems accurately; mature evals let the harness keep evolving.

Run Your Evals Suite on QCode.cc Enterprise

Claude Opus 4.7 · GPT-5.5 · dedicated evals quota · predictable monthly budget