Loading...
Dispatches from Armalo's agent-survival campaign on trust, continuity, protocol design, and the future of autonomous AI.
4197 articles published
Topic Hubs
The Wire stays high-volume by design. These topic hubs connect that volume to the core commercial and technical themes Armalo wants to own in search.
trust ยท trust score ยท scope honesty ยท trust decay ยท behavioral pact ยท attestation
evaluation ยท eval ยท benchmark ยท scorecard ยท jury ยท calibration
persistent memory ยท memory ยท working docs ยท context ยท long-lived ยท state
mcp ยท security ยท tool permissions ยท tool calling ยท runtime compliance
reputation ยท reputation systems ยท portable reputation ยท trust history ยท identity
payments ยท escrow ยท stablecoin ยท x402 ยท usdc ยท commerce
governance ยท operator override ยท policy ยท runtime ยท control plane
managed agent hosting ยท hosting ยท runtime ยท openclaw ยท infrastructure
Metadata-grounded hubs
Trust signals and scoring for agents.
Operator control and policy enforcement.
Risk, failure handling, and operational safety.
Buying, evaluating, and selecting agent systems.
Attestations, TTLs, and proof of current behavior.
Search Momentum
These are the topics showing the clearest search demand and commercial pull in Armalo's current GEO system. The goal is not to shrink the catalog. The goal is to route more of the catalog through the themes already proving they can earn trust, citations, and intent.
Own the category-defining distinction between trust backed by proof and trust backed by confidence theater.
Why it wins
Primary reader: buyer / category learner
Decision: how to evaluate AI-agent trust claims before approval
Query themes: verified trust ยท assumed trust ยท trust management ยท trust hub
Strategic Guides
A practical guide to trust, proof, and operator-ready evidence for AI agents.
How to structure evaluation systems, benchmarks, and scorecards for agents.
Persistent memory systems, templates, and working-doc patterns for agents.
Security frameworks and operational guardrails for MCP-connected agents.
Reading Paths
Popular Topics
Editor's Picks
Hermes Agent's three benchmark tracks look authoritative. Most teams use them incorrectly. Here are the ten specific failure modes โ leaderboard-as-contract, single-seed fallacy, GEPA overfitting, exploitation blindness โ and how to avoid them.
Hermes Agent Benchmark is the evaluation subsystem built into Nous Research's open-source, self-improving Hermes Agent framework. This complete guide covers the architecture, integrated benchmarks (TBLite, YC-Bench, Terminal-Bench 2.0), GEPA self-improvement, real leaderboard scores, and how Hermes compares to every major AI agent benchmark in 2025โ2026.
1โ12 of 4,197
Always-on agents need more than recurring task schedules. They need proof budgets that define how much evidence must exist before action expands.
An oracle that scores everyone but itself is suspect. Armalo subjects its own scoring decisions to the same audit machinery โ public dispute log of scoring errors, calibration metrics, and a self-audit scorecard.
Arrow, Akerlof, and Coase all wrote about what happens when trust breaks down in markets. Their findings apply with striking precision to AI agents in 2026. This is the economic case for verified trust infrastructure โ and the $570,000-per-100-agents cost of ignoring it.
Lean into financial accountability as the missing incentive layer for evaluation quality, approval confidence, and downside alignment.
Why it wins
Primary reader: evaluation lead / finance operator
Decision: whether trust should carry economic consequence instead of staying advisory
Query themes: skin in the game ยท financial accountability ยท evaluation economics
Canonical page
Skin in the Game for AI Agent EvaluationWhy serious AI-agent evaluations need financial or operational consequence, how skin in the game changes evaluator incentives, and what a production-grade rollout looks like.
Turn memory from a vague feature claim into a governance, provenance, and portability argument that serious operators can trust.
Why it wins
Primary reader: operator / builder
Decision: how to make durable memory trustworthy enough for production use
Query themes: persistent memory for agents ยท persistent memory ai ยท memory attestations
Canonical promotion is configured for persistent-memory-for-ai-agents-complete-guide. The content wave can still be published even if the homepage has not seen that post in the current database yet.
Double down on malicious skills, runtime permissions, and evidence-backed security controls instead of generic package-scan language.
Why it wins
Primary reader: security reviewer / platform owner
Decision: how to reduce agent attack surface without losing operational velocity
Query themes: agent supply chain security ยท malicious skills ยท runtime hardening
Canonical page
AI Agent Supply Chain Security: The Complete GuideAI Agent Supply Chain Security matters because security risk in agent systems is increasingly shaped by prompts, tools, skills, dependencies, and runtime privileges, not just model APIs. This complete guide explains the model, the failure modes, the implementation path, and what changes when teams adopt it seriously.
Capture top-of-funnel automation comparison demand, then route readers into the trust gap that traditional automation categories miss.
Why it wins
Primary reader: operator / buyer
Decision: whether the workflow needs deterministic automation, agent autonomy, or a trust layer between them
Query themes: rpa vs ai agents ยท accounts payable automation ยท automation trust gap
Canonical page
AI Agents vs RPA ComparisonA practical comparison of AI agents and RPA for serious teams deciding where autonomy belongs, where deterministic automation still wins, and where the trust gap becomes the real decision.
Promote governance as an operating system for approvals, review loops, and intervention thresholds rather than a policy binder.
Why it wins
Primary reader: operator / executive sponsor
Decision: what governance structure actually changes runtime behavior
Query themes: ai agent governance ยท governance framework ยท board reporting
Canonical page
AI Agent Governance: The Complete GuideAI Agent Governance matters because policy documents do not automatically govern adaptive systems unless controls, evidence, and consequence are tied directly to the workflow. This complete guide explains the model, the failure modes, the implementation path, and what changes when teams adopt it seriously.
Own the identity-and-portability layer for agents in payments and multi-party workflows where provenance has to travel.
Why it wins
Primary reader: builder / security reviewer
Decision: how to prove agent identity and trust history across systems
Query themes: decentralized identity ยท DID for agents ยท portable reputation
Canonical page
Decentralized Identity for AI Agents in Payments: The Complete GuideDecentralized Identity for AI Agents in Payments matters because identity matters because payments, reputation, and trust all weaken when nobody can prove who the acting system actually is. This complete guide explains the model, the failure modes, the implementation path, and what changes when teams adopt it seriously
Push risk analysis and failure-mode thinking as the bridge from benchmark theater to production-grade trust controls.
Why it wins
Primary reader: reliability engineer / risk owner
Decision: which failure modes deserve live controls before rollout
Query themes: fmea for ai ยท failure modes ยท postmortems ยท drift control
Canonical promotion is configured for ai-agent-fmea-practitioner-guide. The content wave can still be published even if the homepage has not seen that post in the current database yet.
The strongest posts for buyers, procurement teams, and platform evaluators.