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Blog Topic
Buying, evaluating, and selecting agent systems.
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.
Enterprise buyers should ask agent vendors for mission control artifacts, not just model benchmarks and polished workflow demos.
A buyer-focused diligence guide for evaluating Agentic OS vendors before agents receive operational authority, tools, or customer-facing scope.
The definitive B2B procurement framework for CIOs and CISOs buying AI agents — covering EU AI Act compliance, 25 RFP questions with scoring rubrics, 15 must-have contract clauses, a 10-metric KPI framework, and a red team protocol that separates production-ready agents from vendor theater.
Agent buyers need a public guide that turns prestige into inspectable evidence, not another ranking that freezes a fast-moving market.
Procurement teams evaluating AI agents face a benchmark landscape built for researchers, not buyers. This guide covers what Hermes benchmarks actually measure, 15+ RFP questions that expose leaderboard theater, how to run pass^k reliability tests, and what a trustworthy vendor submission looks like.
What serious buyers should ask, verify, and refuse when evaluating runtime enforcement in AI agent vendors, platforms, and marketplace listings.
What Buyers Should Ask When a Frontier Model Vendor Shares Less Each Release. Written for buyer teams, focused on how procurement should respond to shrinking disclosure, and grounded in why trust infrastructure matters more as frontier-model transparency gets thinner.
What serious buyers should ask, verify, and refuse when evaluating counterparty proof in AI agent vendors, platforms, and marketplace listings.
What serious buyers should ask, verify, and refuse when evaluating breach response in AI agent vendors, platforms, and marketplace listings.
What serious buyers should ask, verify, and refuse when evaluating measurable clauses in AI agent vendors, platforms, and marketplace listings.
Agentic shopping is not just convenience. It turns budget, merchant policy, substitutions, returns, and receipts into runtime controls.
The agent economy will not mature until buyers can answer a blunt question: when an autonomous action causes loss, who absorbs it and by what proof?
Buyer-scorecard analysis of Agentic OS Mission Control, Armalo Agent recursive self improvement, governed autonomy, trust evidence, and real-world AI operations.
Control Mapping for AI Agent Procurement through a code and integration examples lens: how to map trust controls to buyer concerns so vendor review stops feeling abstract.
Control Mapping for AI Agent Procurement through a benchmark and scorecard lens: how to map trust controls to buyer concerns so vendor review stops feeling abstract.
AI Agent Supply Chain Security and Malicious Skills through the procurement questions lens, focused on which questions expose weak vendors, shallow claims, or missing infrastructure quickly.
Control Mapping for AI Agent Procurement through a architecture and control model lens: how to map trust controls to buyer concerns so vendor review stops feeling abstract.
Control Mapping for AI Agent Procurement through a comprehensive case study lens: how to map trust controls to buyer concerns so vendor review stops feeling abstract.
Control Mapping for AI Agent Procurement through a security and governance lens: how to map trust controls to buyer concerns so vendor review stops feeling abstract.
Procurement Red Flags for AI Agents through a buyer guide lens: the early warning signs that a vendor has capability but not trust infrastructure.
Control Mapping for AI Agent Procurement through a economics and accountability lens: how to map trust controls to buyer concerns so vendor review stops feeling abstract.
Control Mapping for AI Agent Procurement through a failure modes and anti-patterns lens: how to map trust controls to buyer concerns so vendor review stops feeling abstract.
Procurement Red Flags for AI Agents through a comprehensive case study lens: the early warning signs that a vendor has capability but not trust infrastructure.
Procurement Red Flags for AI Agents through a code and integration examples lens: the early warning signs that a vendor has capability but not trust infrastructure.
A self-improving agent is supposed to read its own output, find what is wrong, and fix it — looping toward correctness without supervision. We tested whether that loop converges. One reasoning model produced 40 constraint-bound outputs, then revised each across three rounds under two regimes that differ in exactly one thing: whether an external deterministic checker tells it which constraints failed. Unanchored self-revision repaired 6 of 22 round-0 failures (27.3%); the checker-anchored arm, same model, same outputs, repaired 14 (63.6%) — a 36.4-point recursive-self-improvement ceiling gap (exact McNemar p = 0.0215), and the gap widened every round rather than closing. The mechanism is not weak correction but absent detection: on 16 of the 22 failures the self-revising model never changed a single field, because it did not perceive an error to fix. Self-revision is a detection ceiling, and an external verifier is what raises it. For anyone shipping an autonomous improvement loop, the result is a design rule: bind the loop to a deterministic proof gate, because the agent's own judgment recovers less than half the available repair.