The Problem Every Team Hits
You ship an agent. It has tools, prompts, a model, maybe some skills. Then a developer:- Renames
search_docs→search_knowledge_base(tool registry change) - Updates the system prompt to include a new persona (prompt stack change)
- Removes
compare_competitors(tool removal)
Illustrative figures. Actual counts depend on your trace volume and how the change touches each surface. See the canonical Impact Report example for a representative breakdown.
The Structural Differentiator: Manifest-Aware Detection
Other tools detect regressions by running your eval suite. That requires writing eval cases (most teams haven’t), maintaining them (they go stale silently), running the agent in CI (slow, costly, non-deterministic), and paying for LLM-graded judgment. DecimalAI detects regressions by diffing the manifest and querying the trace store. No eval cases. No agent execution. No LLM API keys. Cost per check: <$0.001.Regression testing, reframed
“Regression testing” usually means eval-based regression testing: keep a golden set of cases, re-grade them after every change, and watch the scores. It answers “did quality drop?” — but only for the cases you thought to write, only after you run the agent, and only if the eval set is still current. DecimalAI’s regression check answers a different, earlier question: “what did this change structurally touch, and which production traces are affected?” It runs on the manifest diff before deploy, with no agent execution and no graded judgment. Severity is reported as HIGH / MEDIUM / LOW IMPACT per trace — a representative diff lands at 247 HIGH / 501 MEDIUM / 1,254 LOW across 2,002 traces. The two are complementary, not competing. Eval-based testing measures quality on a curated set; manifest-aware regression measures blast radius on real traffic. Most teams have the second gap, not the first — which is why DecimalAI leads with it.IMPACT (HIGH / MEDIUM / LOW) answers “was this trace structurally touched?” — a separate axis from the compatibility verdict (keep / repair / flag / replay / drop), which answers “what should I do with the trace for training?”
Feature Comparison
Where Each Tool Excels
Every tool here is good at something. The honest read:
DecimalAI’s edge comes from the structural identity layer:
- Catch regressions without writing eval cases (GitHub Action on every PR)
- Measure skills with production effectiveness data (pass rates, activation trends)
- Keep training data valid when the agent changes (auto-classify + repair)
The ROI of Manifest Awareness
The clearest proof is the artifact itself. When a manifest change lands, DecimalAI produces an Impact Report — every affected trace bucketed by IMPACT severity, with a per-trace compatibility verdict (keep / repair / flag / replay / drop):Scenario: 10-Agent Production System
Illustrative figures for a representative team — your numbers will vary with update cadence, trace volume, and how often changes touch high-traffic surfaces.
When to Use DecimalAI
DecimalAI is the right choice if:You ship agent changes regularly and want to catch regressions before deploy
You use skills/instructions and want to know which ones actually work
You fine-tune models and need version-aware training data
Your agent’s tools, prompts, or models change frequently
You run multi-agent systems and need to track cross-agent drift
- You only need basic LLM tracing with no change management workflow → Consider Langfuse
- You’re exclusively in the LangChain ecosystem and need hub features → Consider LangSmith
- You only need evaluation scoring without production tracing → Consider Braintrust
Getting Started
Pick the path that matches your immediate need:Catch regressions
Most common entry point. Manifest impact analysis on every PR — no eval cases required.
Track skills
Effectiveness analytics, smart routing, public registry with SkillScore.
Build training data
Versioned SFT datasets that stay valid as the agent evolves.