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09/07/2026 3:16 pm
TL;DR: Agentic AI systems are stateful, continuously operating, and layered, which makes restoration by clean backup an incomplete recovery model, according to Commvault. The real failure is coherence: memory, runtime workflows, observability, and inter-agent trust all have to be verified together before recovery can be trusted.
NHIMG editorial — based on content published by Commvault: Agentic AI resilience and recovery models
By the numbers:
- Only 1 in 5 companies has a mature model for governing autonomous AI agents.
- Only 17% of continuously monitor agent-to-agent interactions.
Questions worth separating out
Q: How should security teams govern agentic AI systems that retain memory across sessions?
A: Governance should treat memory as part of the decision surface, not just storage.
Q: Why do agentic AI systems complicate traditional recovery and access review models?
A: They complicate both because behaviour is continuous, stateful, and decision-driven.
Q: What breaks when agent-to-agent interactions are not fully observable?
A: The causal chain breaks.
Practitioner guidance
- Map agent trust boundaries across the full workflow Inventory where memory, orchestration, tool access, and subagent handoffs influence each decision path.
- Instrument memory-layer integrity checks Version-control embeddings and session context so you can prove what the agent believed at a specific point in time.
- Capture agent-to-agent interactions in the audit trail Log context transfer, delegated outputs, and cross-agent tool invocations as explicit events.
What's in the full article
Commvault's full analysis covers the operational detail this post intentionally leaves for the source:
- Layer-by-layer discussion of memory, runtime control, observability, and multi-agent coordination failure modes.
- Examples of how compromised vector databases and corrupted planning inputs change agent behaviour in practice.
- The recovery model for restoring a trustworthy state across model, memory, orchestration, and identity layers.
- The article's FAQ section with direct answers on why stateless recovery fails for agentic systems.
👉 Read Commvault's analysis of agentic AI resilience and recovery models →
Agentic AI resilience gaps: what identity teams are missing now?
Explore further
09/07/2026 3:33 pm
Agentic recovery is a coherence problem, not a backup problem. The article’s central point is that clean data alone cannot restore trust when state is distributed across memory, workflow, identity, and interaction layers. That matters because recovery controls built for stateless systems assume a single clean source of truth, while agentic environments require the truth of multiple layers at the same moment. Practitioners should treat recovery as state reconciliation across the agent stack.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), according to AI Agents: The New Attack Surface report.
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: How do organisations know when an agentic AI recovery process is actually trustworthy?
A: A recovery process is trustworthy only when the model version, memory contents, orchestration state, and identity permissions are aligned to the same verified point in time. If any layer is stale or unverified, the environment may look restored while still carrying corrupted decision inputs.
👉 Read our full editorial: Agentic AI resilience depends on identity, memory and workflow trust
