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You built the AI.
We make sure it keeps working.
Production is where agentic systems earn their keep, and where they quietly fail. ThoughtMinds takes over once development is done: testing, evaluation, observability, and continuous improvement so your AI stays reliable at scale.
Featured stories
01Agentic Al | Automated Testing | Regression CoverageHow an Agentic Workflow Stayed Reliable Through Three Model Upgrades Tag
From Production Incident to Permanent Regression Test in Under 48 Hours Tag
Catching Silent Prompt Degradation Before It Reached End Users Tag
Seven areas. One ops layer.
Everything that happens after your AI goes live, from baseline to continuous improvement.
Handover & baseline establishment
Before we run anything, we learn your system properly, through a guided onboarding conversation, not a form.
Reads your docs, configs, and traces to capture your tech stack, agent setup, and operational baselines, no re-explaining what's already documented- Defines success and failure explicitly: latency thresholds, cost limits, and hard behavioural boundaries your system must never cross
- Everything gets captured in a versioned System Contract — the agreed reference point for all future testing and monitoring
Automated Testing of Agentic Workflows
Agentic failures are often multi-step — plausible-looking chains that quietly reach the wrong result. We test full scenarios end-to-end, not just individual outputs.
- Tests are built from your system spec and expanded into adversarial, stress, and regression variants — nothing generic
- Every scenario runs as a full arc in a sandboxed mirror of production, scored across goal completion, tool use, safety, and constraint adherence
- Testing re-triggers automatically on prompt changes, model swaps, or workflow edits — continuous assurance, not a pre-launch gate
Continuous Evaluation & Quality Measurement
The question isn't whether your system worked at launch. It's whether it's still working now.
- Every risk-flagged interaction gets full evaluation - never sampled away regardless of traffic volume
- Quality is tracked across goal completion, reasoning, tool use, and safety compliance, with sampling rates that automatically adjust when shifts are detected
- A live scorecard shows performance trends and baseline comparisons - you see the signal before it becomes a user-facing problem
Observability & Real-World Behaviour Monitoring
Evaluation tells you what quality looks like from the outside. Observability looks inside — at the paths, decisions, and patterns that produced it.
- Works with trace data your system already emits — compatible with OpenTelemetry, LangGraph, LangSmith, and others. No new instrumentation needed
- Monitors execution paths, tool call sequencing, token consumption, memory coherence, and multi-agent delegation — with behavioural drift detected across distributions over time
- Acute anomalies flagged in real time; Trace Explorer lets you drill into any individual run and see every LLM call, tool invocation, and decision point in sequence
Issue Identification & Root Cause Analysis
Agentic failures are emergent, probabilistic, and often multi-causal. We correlate signals across evaluation, observability, and execution traces to diagnose the actual cause — not just the symptom.
- Issues are classified into a clear taxonomy — quality regression, tool failure cascade, prompt degradation, memory failure, multi-agent coordination failure — each with a severity level
- A dedicated RCA agent produces ranked, falsifiable hypotheses with evidence and confidence scores — not a black-box verdict
- Every confirmed root cause generates new test cases — the test suite gets smarter with every production issue resolved
Fix Recommendations & Continuous Improvement
Diagnosis without action is just observation. Fixes are concrete, validated, and delivered as structured packages — not general guidance.
- Every root cause maps to a specific fix: exact prompt rewording, tool configuration change, workflow adjustment, or guardrail update — with expected impact stated upfront
- Each fix is validated before closure: regression tested, before/after evaluation compared, and monitored closely for 24–72 hours post-deployment
- A System Health Score tracks reliability over time — and compounds: every resolved issue sharpens detection and builds the fix library, making future failures less likely and faster to close.
Retraining & asset feedback loop
Production is the most valuable data source your system will ever have. We surface it back as ready-to-use assets — so every cycle makes your system fundamentally smarter, not just more stable.
- Confirmed high-quality interactions packaged as fine-tuning data, well-handled edge cases shaped into few-shot examples, and graceful recoveries turned into workflow refinement blueprints — labelled, traced, and ready to use
- We identify which prompt examples are working and replace underperforming ones with real production interactions confirmed to perform better
- Every production cycle compounds capability: the model learns, the prompts sharpen, the workflows improve — turning reliability into a growth curve, not just a baseline
We embed at the ops layer. Your build stays yours.
ThoughtMinds doesn't replace your AI stack or your development team. We sit above it consuming the traces and signals your system already emits, building the observability and testing layer your team doesn't have time to build themselves. Every fix recommendation is concrete and validated before it goes anywhere near production. Every confirmed issue becomes a permanent regression test. The system gets more reliable the longer we work together.
