Agentic AI

This paper introduces latent equivalence learning to bridge the gap between semantic reasoning and structure prediction in enterprise data agents. Instead of relying on reusable markdown-style memory files, the framework separates persistent task-relevant identities from recurring structural patterns. This division of labor allows agents to focus on reasoning while learned systems handle the organization of complex, distributed data schemas and relationships.

• Replaces static markdown memory with learned structural prediction for recurring data patterns.
• Separates semantic reasoning from schema organization to improve agent efficiency.
• Addresses complex enterprise environments with distributed schemas and business roles.
• Reduces redundant discovery by persisting task-relevant identities via latent equivalence.

A new arXiv study reveals that repeated agent tasks suffer from high inconsistency, with up to 74% of outputs disagreeing across runs. To address this, the authors propose 'skill habit formation,' where agents analyze their own execution history to extract deterministic skill variants. These candidates compete against existing methods by declaring specific input regions, allowing common cases to execute as fast scripts while falling back to reasoning for edge cases.

• Re-running identical tasks yields inconsistent results in 38-74% of cases across different models.
• Skill habits are mined from execution history rather than manually coded by developers.
• Candidates declare their input space regions to compete with incumbent methods dynamically.
• Common cases execute as deterministic scripts, while complex inputs fall back to reasoning.
• This approach aims to reduce the 95%+ waste from re-deriving known plans.

ZeroGate decouples authorization from admission to shorten dispatch boundaries without removing security checks. An issuer signs a short-lived ActionPass, which a trusted runtime adapter reconstructs and validates against a local gate using a nonce and binding check. A SQLite transaction ensures atomicity for nonce consumption, quota updates, and admission receipts, preserving synchronous policy decisions.

• Decouples authorization from admission to reduce agent dispatch latency.
• Uses short-lived ActionPass signatures for fast, traceable local validation.
• SQLite transactions atomically handle nonces, quotas, and admission receipts.
• Maintains trust by ensuring local admission implies synchronous policy approval.

AWS Continuum now allows security teams to validate login credentials and receive accessible domain suggestions before starting a penetration test. This proactive validation helps define an accurate network scope immediately, preventing misconfigurations that previously required manual effort. By catching authentication failures and identifying valid targets early, the tool eliminates wasted test cycles and accelerates the secure development lifecycle.

• Validate login credentials during test configuration to ensure the agent can authenticate successfully.
• Receive suggested domains upfront to define an accurate network scope without manual discovery.
• Reduce wasted test cycles by catching authentication failures before the full penetration test runs.
• Minimize configuration errors that lead to incomplete or inaccurate security assessments.