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Is Your Organisation
Is Your Organisation
Agentic Ready?
95% of enterprise AI agent projects never reach production. The blocker isn’t technology — it’s governance. Find out where you stand in 5 minutes.
95% of enterprise AI agent projects never reach production. The blocker isn’t technology — it’s governance. Find out where you stand in 5 minutes.
An AI agent is an autonomous software actor that interprets goals, reasons through context, and takes actions across tools and systems on behalf of users. Unlike traditional AI models that just answer questions, agents execute — they browse, decide, and interact with your systems.
An organisation is agentic ready when it has the governance, identity, and compliance infrastructure in place to deploy AI agents safely and at scale — not just as isolated pilots, but as production capabilities that reach end users and business systems.
The gap between piloting agents and deploying them isn't a technology problem. It's a governance problem.
Teams are experimenting with agents without audit trails, oversight, or security review.
Manual approvals and ad-hoc access controls create bottlenecks that kill agent projects in POC.
Without audit trails and automated policy enforcement, regulators won’t let agents near production.
Every organisation is somewhere on this curve. Take the assessment to find out where you are — and what it takes to move forward.
Every enterprise falls somewhere on this curve. The question isn't whether — it's where, and how fast you can move.
| 01 Ad Hoc | 02 Emerging | 03 Managed | 04 Autonomous | |
|---|---|---|---|---|
| Agent visibility | None — shadow AI | Partial — known pilots only | Centralised registry | Full portfolio inventory |
| Identity model | Shared credentials | Inconsistent per team | Non-human identity for every agent | Adaptive identity with risk context |
| Policy enforcement | Ad hoc / none | Manual review per deploy | Automated guardrails | Dynamic, risk-based policy |
| Audit & compliance | No trail | Reactive, on request | Continuous audit logs | Always audit-ready |
| Time to production | N/A — POCs only | Quarters, if ever | Weeks | Days |
| Business outcome | Unmanaged risk | POC purgatory | Safe scale | Structural advantage |
Answer a few questions about how your organisation handles AI agents today. You'll get your maturity stage and what to focus on next.
This is where most organisations start. Individual teams are experimenting with AI tools and occasionally agents, but there’s no central visibility, no governance, and no consistent approach.
The risk isn’t inaction — it’s unmanaged action. Shadow AI is happening across your organisation. Without a registry of what agents exist, what data they access, and what actions they take, you’re accumulating risk with every experiment.
Getting out of this stage means acknowledging that AI agents aren’t just another tool — they’re autonomous actors that need identity, oversight, and boundaries.
No inventory of agents or AI tools across the org
Agents use shared credentials or personal tokens
No record of what agents did or what data they touched
Experiments driven by individuals, not the business
Think your organisation might be here?
Take the AssessmentThis is the most dangerous stage — and where 95% of organisations get stuck. You’ve launched POCs, but manual approval processes, ad-hoc access controls, and inconsistent compliance create bottlenecks that prevent anything from reaching production.
The pattern repeats: a team builds an impressive agent demo, stakeholders get excited, then legal, security, and compliance raise questions nobody can answer. The project stalls. Another team starts a different POC. The cycle continues.
Breaking through requires intentional agent governance — a basic registry, preliminary identity framework, and initial compliance posture are the minimum to move forward.
Multiple pilots, none graduating to production
Every agent deployment needs manual security review
Each team invents their own approach to agent security
Executive enthusiasm fading as projects fail to launch
Stuck in POC purgatory?
Take the AssessmentThe breakthrough stage. The organisation has a centralised agent registry, automated policy enforcement, proper identity and access management for non-human actors, and audit trails that satisfy compliance.
Agents don’t need months of review to reach production. Guardrails are automated, not bureaucratic. Security and compliance teams have visibility, and deployment pipelines exist for agent workloads.
New agent use cases go from concept to production in weeks, not quarters. The organisation can say yes because the infrastructure makes it safe.
Centralised registry, identity, and policy management
Policy enforcement that scales — not manual reviews
Every agent action logged, traceable, compliant
New agent use cases reach production fast
Ready to build your control plane?
Take the AssessmentThe end state. Multi-agent orchestration is standard. Governance adapts dynamically based on risk context and agent behaviour. Compliance is continuous, not periodic. The agent portfolio delivers measurable ROI.
At this stage, agents aren’t a project — they’re an operating capability. New agents inherit governance by default. The organisation scales agent deployments as fast as it can identify use cases.
Very few organisations have reached this stage today. Those that do will have a structural advantage in the agentic era.
Agents collaborate within governed boundaries
Policies adjust dynamically based on risk context
Always audit-ready, not just at review time
Clear measurement of business value across the portfolio
Building towards autonomous governance?
Take the AssessmentEverything you need to know about deploying AI agents safely at enterprise scale.
An AI agent is an autonomous software actor that interprets goals, reasons through context, and takes actions across tools and systems on behalf of users. Unlike traditional AI models that only produce answers, agents execute actions across your stack — browsing, deciding, and interacting with systems autonomously.
Agent governance is the set of identity controls, policies, and audit mechanisms that let organisations deploy AI agents safely at scale. It answers four questions at all times: which agents exist, what can they access, what did they do, and who authorised them to do it.
Most fail not for technical reasons but because organisations lack the governance frameworks, identity controls, and compliance infrastructure to move agents from proof-of-concept to production. Manual approvals, ad-hoc access controls, and inconsistent compliance create bottlenecks that kill agent initiatives in POC.
The Agentic Ready maturity curve defines four stages: Ad Hoc (experimentation without visibility or governance), Emerging (POCs stuck behind manual bottlenecks), Managed (centralised control plane with automated policy enforcement), and Autonomous (multi-agent orchestration with adaptive governance at scale).
An agent control plane is the centralised infrastructure layer that manages agent identity, policy enforcement, audit trails, and lifecycle for every agent in an organisation. It is the difference between “we have some pilots” and “we can deploy new agents safely in weeks, not quarters.”
Breaking through POC purgatory requires four things: a centralised registry of every agent, automated identity and access controls for non-human actors, continuous compliance signals rather than periodic audits, and deployment pipelines built for agent workloads. The Agentic Readiness Assessment identifies where your organisation is today and what to build next.
Prefactor helps enterprises close the gap between AI experiments and AI agents in production. We build the governance frameworks, control planes, and operational infrastructure that move organisations from Stage 1 to Stage 4.
This is our open resource — raising the bar for how enterprises think about agent governance before they deploy.
Learn more about Prefactor →Map your maturity and build a roadmap to production
Centralised governance infrastructure for enterprise agents
Purpose-built compliance for autonomous AI systems
Break through where 95% of agent projects fail
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