Explainers
Founder Videos
Short, no-fluff walkthroughs of the problem, the category, and how MeaningStack works.
Practical guides, deep dives, and answers to common questions about Operational Agent Governance, Blueprint-driven reasoning, and reliable AI in production.
Production-first thinking on the real failure modes of autonomous systems.
Why agents fail in reasoning (not outputs), how Blueprints normalize internal maps, and how CTQ + Steward Agents prevent drift in production.
Read article →A clear walkthrough of how Blueprints encode required checks, risk comparisons, and safe tool usage.
Coming soon →Why distributions matter more than thresholds, and how to build production trust baselines for agents.
Coming soon →Short, no-fluff walkthroughs of the problem, the category, and how MeaningStack works.
Straight answers for ML, DevOps, Security, and Compliance teams evaluating MeaningStack.
MeaningStack is the real-time operational governance layer for autonomous AI agents, giving you visibility into reasoning and control before decisions become actions.
Observability tracks outcomes after the fact. MeaningStack governs the reasoning loop in real time — catching unsafe assumptions, skipped checks, and policy drift before actions execute.
Guardrails filter prompts or outputs. MeaningStack governs cognition: it evaluates whether the agent reasoned safely and completely, regardless of how fluent the output looks.
No. MeaningStack is model-agnostic and framework-agnostic. You integrate at the runtime layer (e.g., via MCP/A2A), without retraining or rewriting your agent logic.
Blueprints are symbolic maps of safe reasoning for a domain. They define required checkpoints, risk comparisons, and tool preconditions so agents don’t operate with blind spots or missing steps.
No. Blueprints don’t prescribe a path; they define the terrain. Agents remain autonomous inside known-safe boundaries, while unsafe shortcuts are flagged or blocked.
Steward Agents are runtime monitors that score reasoning quality, detect drift, and trigger risk-scaled interventions. They automate governance at production scale.
With CTQ (Cognitive Trace Quality), a real-time score for safety, completeness, and policy alignment. The key signal is CTQ distributions over time, which reveal drift and trust baselines.
A blind spot is a missing check or hidden assumption in the reasoning loop — often invisible in outputs — that leads to unsafe tool calls or high-risk decisions.
Oversight scales to risk. Low-risk actions receive ultra-light monitoring. Deep analysis and human review trigger only for high-stakes decisions, minimizing token overhead and latency drag.
MeaningStack preserves traceability across agent hand-offs through A2A protocols, avoiding black-box “agent-as-tool” blind spots.
MeaningStack provides runtime audit trails, pre-action interventions, and policy-encoded Blueprints — the operational evidence layer needed for high-risk AI compliance.
Integration support, Blueprint setup for your workflows, CTQ baselines, and a production dashboard that shows reasoning failures, drift, and intervention outcomes.
Most teams integrate in hours to a few days depending on stack complexity. No model retraining is required.
Start with high-value workflows where risk matters: financial ops, healthcare decisions, customer-facing agent actions, or multi-agent enterprise automation.
We can share deeper technical notes, sample Blueprints, and production CTQ dashboards for your domain.
Request a demo Subscribe