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18/08/2026 2:09 pm
TL;DR: Top agentic AI platforms should be evaluated on orchestration, governance, observability, and operational fit rather than demo appeal, TruFoundry says, as 2026 buyers consider systems spanning enterprise automation, multi-agent development, and no-code workflows. The broader implication is that autonomous execution changes the identity and control model around AI systems, not just the interface layer.
NHIMG editorial — based on content published by TruFoundry: Best Agentic AI Platforms and Tools You Can Try in 2026
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope.
Questions worth separating out
Q: How should security teams govern AI agents that can access enterprise systems?
A: Security teams should govern AI agents as non-human identities with explicit ownership, scoped privileges, and continuous monitoring.
Q: Why do AI agents complicate existing IAM and NHI governance models?
A: AI agents complicate governance because access is no longer confined to a single environment or a single identity type.
Q: What breaks when AI agent access is reviewed only after the fact?
A: After-the-fact review leaves a gap between action and containment.
Practitioner guidance
- Define the agent trust boundary Map every model, tool, connector, and downstream system the platform can reach, then assign ownership for each trust relationship.
- Separate routing from authorization Require the gateway to enforce policy decisions before tool execution, not after the model has already selected an action path.
- Inventory all agent credentials and connectors Track every API key, token, certificate, and service account the platform uses, including temporary integrations created during experiments.
What's in the full article
TruFoundry's full article covers the platform-by-platform operational detail this post intentionally leaves for the source:
- Hands-on feature comparisons for each agentic AI platform, including where orchestration, governance, and observability differ in practice.
- Platform-specific positioning for enterprise deployment, including which tools are better suited to multi-agent systems, no-code workflows, or RPA-led automation.
- Implementation notes on routing, model deployment, and monitoring that practitioners need once they move from category evaluation to build decisions.
- Vendor descriptions of framework compatibility and workflow support for LangGraph, CrewAI, AutoGen, and related stacks.
👉 Read TruFoundry's roundup of the best agentic AI platforms for 2026 →
Agentic AI platforms in 2026: what control gaps matter most?
Explore further
18/08/2026 3:02 pm
Agentic AI platforms are becoming identity-control planes, not just application tooling. Once a platform brokers routing, tool use, and workflow execution, it sits inside the trust boundary that IAM and NHI teams used to reserve for privileged infrastructure. That changes how access, logging, and policy enforcement need to be thought about. Practitioners should treat the platform as part of the identity estate, not an adjacent product layer.
A few things that frame the scale:
- 98% of companies plan to deploy even more AI agents within the next 12 months, despite documented rogue behaviour in 80% of current deployments, 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: Which controls matter most for production AI gateway governance?
A: The most important controls are tool authorization, credential inventory, observability, and lifecycle revocation. Without those, the gateway becomes a powerful but poorly governed trust broker. Teams should be able to see which agent used which credential, touched which system, and under what policy condition.
👉 Read our full editorial: AI agent platform governance now spans routing, observability, and control
