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18/08/2026 2:45 pm
TL;DR: Agentic AI performance does not improve simply by adding more agents, according to Holistic AI’s analysis of Google and MIT research across 180 experiments. The results show that application fit, coordination overhead, and observability determine whether multi-agent systems improve outcomes or become slower, costlier, and harder to govern.
NHIMG editorial — based on content published by Holistic AI: Don't Throw Good Agents After Bad: Smarter Agentic AI Deployment
By the numbers:
- Parallelized applications saw performance gains of up to 81% over a single-agent baseline.
- Serialized applications saw performance degrade by up to 70% when multiple agents were added.
- Single agents averaged 67 successful tasks per 1,000 tokens, while centralized multi-agent systems managed just 21.
Questions worth separating out
Q: How should security teams govern AI agents that run long, multi-step workflows?
A: Security teams should require durable execution, full event history, and clear ownership for every multi-step agent workflow that touches sensitive data or privileged tools.
Q: Why do multi-agent systems sometimes perform worse than a single agent?
A: They often fail when the task is sequential rather than parallel.
Q: What do organisations get wrong when scaling agentic AI?
A: They often treat agents as workflow enhancements instead of governed actors with identity, privilege, and audit requirements.
Practitioner guidance
- Classify AI agents as governed non-human identities Assign each agent an owner, purpose, credential boundary, and lifecycle record so that AI access can be reviewed like other high-risk machine identities.
- Gate multi-agent rollout on task dependency analysis Approve parallel agent designs only where workflow steps are genuinely independent, and block expansion where sequential state changes would amplify coordination cost.
- Instrument runtime tracing before production scale Capture agent inputs, outputs, tool calls, and inter-agent handoffs so investigators can reconstruct failures and compliance teams can evidence scope.
What's in the full article
Holistic AI's full blog covers the operational detail this post intentionally leaves for the source:
- The full breakdown of how Holistic AI visualises inter-agent dependencies and workflow bottlenecks during production troubleshooting.
- The specific interpretation of the Google and MIT experiments across different model families, prompts, and token budgets.
- The governance platform detail on lifecycle oversight, monitoring, and control enforcement across the AI lifecycle.
- The vendor's examples of how teams can diagnose when agent coordination cost outweighs automation value.
👉 Read Holistic AI's analysis of smarter agentic AI deployment →
Agentic AI scaling: are observability and governance keeping up?
Explore further
18/08/2026 3:28 pm
Scaling agentic AI is now an identity governance problem, not just an architecture problem. Once agents can plan and act across workflows, they behave like non-human identities with permissions, dependencies, and accountability requirements. That means deployment decisions should be governed with the same seriousness as privileged service accounts and workload identities. The practical conclusion is straightforward: if you cannot govern the agent, you cannot safely scale it.
A question worth separating out:
Q: Why does open governance change the risk profile for agentic AI infrastructure?
A: Open governance reduces single-vendor dependency, but it also removes the false comfort that a proprietary platform will solve identity, authorisation, or audit problems for you. Once standards become shared infrastructure, the organisation owns the control design. That makes policy consistency, logging, and revocation the real differentiators, not the protocol label itself.
👉 Read our full editorial: Agentic AI scaling depends on observability before more agents
