Loading...
Loading...
Loading...
Blog Topic
Risk, failure handling, and operational safety.
24 metadata-ranked posts in this topic
Ranked for relevance, freshness, and usefulness so readers can find the strongest Armalo posts inside this topic quickly.
Agentic incident response needs mission context, tool receipts, permission history, and recursive rollback in one command surface.
Boards do not need mystical dashboards for AGI risk. They need mission-control evidence about authority, drift, incidents, and recourse.
Incident-response analysis of Agentic OS Mission Control, Armalo Agent recursive self improvement, governed autonomy, trust evidence, and real-world AI operations.
AI agents confabulate. They produce fluent, confident-sounding outputs that are factually wrong. In a demo, this is embarrassing. In a customer conversation, a financial analysis, or a compliance review, it is a structural risk that requires architectural solutions, not prompting workarounds.
A leadership lens on failure mode and effects analysis for ai, focused on operating leverage, downside containment, evidence quality, and why executive teams should care before an incident forces the conversation.
The most dangerous fmea for ai systems failures usually do not look obvious at first. This post maps the anti-patterns that create false confidence, hidden drift, and expensive incidents.
How incident review should work for failure mode and effects analysis for ai so teams can turn failures into reusable control improvements instead of expensive storytelling exercises.
The most dangerous failure mode and effects analysis for ai failures usually do not look obvious at first. This post maps the anti-patterns that create false confidence, hidden drift, and expensive incidents.
Enterprise AI deployments are failing at a rate that the industry is not discussing honestly. The failure mode is not technical — it is governance. And the fix is not more capable models.
How incident review should work for fmea for ai systems so teams can turn failures into reusable control improvements instead of expensive storytelling exercises.
Agentic red teams should probe authority ladders, tool receipts, memory provenance, recursive promotions, and incident recovery.
Every autonomous workflow should have a blast-radius budget: a bounded definition of how much money, data, customer impact, and authority it can risk before review.
When websites expose tools to browser agents, trust moves from page content to tool manifests, side-effect labels, and receipts.
When agents do consequential work, disputes are not edge cases. They are the mechanism that lets trust recover, downgrade, or become more credible.
Browser agents will not stay in harmless browsing mode. They need labels that distinguish reading, drafting, submitting, buying, exporting, and deleting.
Autonomous agents need budgets for cost, risk, evidence, authority, and attention before recursive loops can compound responsibly.
Indirect prompt injection is usually framed as input filtering. For consequential agents, it is a planning and authority failure.
A swarm can pass every individual agent eval and still fail when trust, memory, instructions, or tool outputs cascade across agents.
Awards can speed procurement only when buyers inspect category fit, evidence class, freshness, failure history, and post-purchase monitoring.
Governed-RSI analysis of Agentic OS Mission Control, Armalo Agent recursive self improvement, governed autonomy, trust evidence, and real-world AI operations.
Multi-agent swarms amplify what is good and bad about individual agents simultaneously. Getting the intelligence without the risk requires governance architecture designed for distributed autonomous behavior, not retrofitted from single-agent controls.
The most expensive AI failures are not the dramatic ones. They are the slow accumulations of small errors, scope violations, and unverified decisions that enterprises discover only after they have compounded into something impossible to quietly fix.
An operator reliability playbook for the most common Hermes Agent production failures: cron fail-closed, memory overflow, subagent context starvation, MCP probe failures, browser TTL, and provider fallback exhaustion, with concrete triage steps.
Every dependency on a public oracle is a dependency on its uptime. Here are the failure modes you have to design for, and a template for the plan you do not have yet.
Safety Research
A public roadmap for calibrated workspace research across eight evidence gates: calibration, behavior, specificity, entanglement, sparse features, agent telemetry, self-monitoring, and adversarial robustness.