Artificial Intelligence · 06.08.2026, 16:55 UTC
Control agent behaviors and cost beyond a single action: new capabilities in Amazon Bedrock AgentCore
| Schweregrad | info |
|---|---|
| Kategorie | Artificial Intelligence |
| Quelle | AWS Machine Learning ↗ |
| Veröffentlicht | 06.08.2026 UTC |
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Agents are becoming more autonomous and teams are running more of them, but trust and security have not kept pace. According to McKinsey, roughly 80% of organizations have already encountered risky behavior from AI agents. As a result, security and risk concerns are the leading barrier to scaling agentic AI (McKinsey’s State of AI Trust in 2026, and Trust in the age of AI agents 2026). That makes trust the pacing factor for agent innovation and adoption. Earning it takes control across a wide surface, including identity, access, observability, evaluation, and traceability. We believe that investment in trust and security will accelerate agent adoption in enterprises. When guardrails are dependable, approving a new agent stops being a one-off negotiation and becomes something the platform handles at scale. The challenge is that most guardrails today were designed for software that behaves predictably. Agents decide their own path as they go, so every step can pass on its own while the shape of the whole goes unexamined. An agent looks up a customer’s account, then transfers money to a different account number, because each call was judged on its own. An agent places a series of orders that each sits under the approval threshold, because nothing is tracking the total against the budget. An agent hits a failing tool and retries through the night, running through the token budget, because nothing capped how much it could consume. Every one of those requests was legitimate. The problem appears only in the pattern, and the agent is the last thing you would rely on to catch it. …