Introducing the 6-Point Agentic Autonomy Scale From AI Tool to Full Agency
By Dickey Singh
By 2025, ‘agentic’ has morphed into a buzzword—many companies tout their ‘agency’ without truly understanding it or being upfront about what they deliver. Marketers and investors only fan the flames, as everyone’s eager to glimpse what’s possible.
At a high level, agency describes AI’s shift from simply answering queries to taking real-world actions on our behalf, with ever-less human oversight.
At its core, agency means the power to set goals, plan ways to achieve them, and act—without constant hand-holding. But when every system labels itself “agentic” merely for producing an output, it only adds to the confusion. That makes life harder for executives and investors who need to tell real progress from marketing hype.
A decade ago, when self-driving cars first captured our imagination, the term “autonomous” was used so loosely that no one—from engineers to regulators—agreed on what “Level 3” or “Level 5” actually meant. SAE J3016, introduced in 2014, changed that by defining six clear levels (0–5) of vehicle autonomy—and it’s precisely this kind of shared framework that today’s agentic AI needs.
In this article, we propose a concise, SAE J3016–inspired zero-to-five-point framework that defines exactly what it takes to be truly agentic.
Before we dive in, let’s clarify a few key definitions.
Key Definitions
Agentic System (academic):
The academic definition builds on the classic “agent” concept in artificial intelligence (Russell & Norvig, 2010). An agentic system is a depth-first computer program that can:
Formulate its own goals within a set of user-defined constraints
Perceive and model its environment through continuous data gathering and interpretation
Plan multi-step actions by breaking large goals into manageable tasks
Execute those tasks end-to-end, handling errors and recovering as needed
Adapt and learn over time by using feedback to improve its decisions
Agentic System (executive):
At a high level, “agentic” captures AI’s evolution from merely answering queries to autonomously taking real-world actions on our behalf, with ever-less human oversight.
Agency: The capacity to set goals, make decisions, and take actions on your own behalf—without requiring step-by-step instructions.
Agent: An entity (human or machine) that exhibits agency—i.e., one that holds objectives, plans, and acts independently.
Agentic: Describes something (an AI system) that demonstrates agency in practice: it decides what to do, how, and when.
Autonomy: The degree of agency on a spectrum—from zero (no independence) to full (completely self-directed).
Guardrails: The rules, policies, and boundaries that constrain an agent’s behavior—ensuring its actions stay aligned with your objectives, compliant with regulations, and safe for users and systems.
Observability:
The extent to which an AI system’s processes, reasoning, and outcomes are visible and explainable—enabling detection of errors, biases, or unexpected behaviors.
What does Observability mean for executives? Observability ensures transparency and traceability, so teams can see not just what the AI did, but why—and intervene or audit whenever needed.
Inspiration: SAE’s Driving-Automation Levels
In 2014, SAE International published SAE J3016, a taxonomy defining six levels (0–5) of vehicle autonomy—from “no automation” to “full automation” (SAE International, 2014). Regulators and automakers embraced it because it provides:
Clear milestones for R&D and regulation.
Common language for consumers and engineers.
Trust, by spelling out exactly what a “Level 4” car can—and cannot—do.
Most commercial AI today sits between Levels 1–2. For example:
Tesla Autopilot FSD, GM Super Cruise, and Ford BlueCruise are officially classified as SAE Level 2 advanced driver-assistance systems—requiring continuous driver supervision—under NHTSA’s Third Amended Standing General Order on crash reporting (SGO-2021-01), effective April 2025.
Mercedes-Benz Drive Pilot and Honda Legend Traffic-Jam Pilot have achieved SAE Level 3, allowing hands-off highway driving under defined conditions.
Waymo and GM Cruise robotaxis operate at SAE Level 4 within geofenced zones, requiring a human only for exceptions.
SAE 6-point Autonomy Scale
Level
Name
Capability
0
No Automation
A human driver performs all driving tasks—system issues warnings or momentary assistance only.
1
Driver Assistance
The system can assist with either steering or acceleration/deceleration, but the human driver remains fully responsible and must supervise continuously.
2
Partial Automation
The system controls both steering and acceleration/deceleration under defined conditions; human must monitor the environment and be ready to intervene.
3
Conditional Automation
The system handles steering, acceleration/deceleration, and environment monitoring in certain scenarios; human must be prepared to take over when requested.
4
High Automation
The system performs all driving tasks and monitors the environment in specific conditions without human intervention; a human can still request control.
5
Full Automation
The system performs all driving tasks under all conditions—no human intervention required.
These levels build on decades of human-automation research (Parasuraman, Sheridan, & Wickens, 2000; Sheridan & Verplank, 1978) and give everyone a shared reference point. When someone says “Level 5 autonomous car,” you know they mean hands-off, eyes-off driving.
That immediate intuition is precisely what AI Agents need.
Core Elements of Full Agency
Not every intelligent algorithm qualifies as an agent. To claim true agency, a system must satisfy all five of these pillars:
Goal-Directed Behavior The system holds its own objectives (e.g., “boost renewal rate”); it doesn’t merely execute human commands.
Perception & Interpretation It ingests contextual data—customer usage, support tickets, market trends—and makes sense of shifting conditions.
Planning & Decomposition Faced with a high-level goal, it breaks work into sub-tasks (“draft slides → send for review → schedule follow-up”).
Execution & Adjustment It carries out those tasks end-to-end, recovers from hiccups (API errors, ambiguous inputs), or flags when human help is needed.
Adaptation Over Time With each run, it learns from performance metrics and human feedback, improving reliability on the next cycle.
If any pillar is missing, the system is still a tool or an assistant—helpful, but not fully agentic.
Why Agency Matters in AI
Scale True agents can juggle thousands of distinct workflows in parallel. No human team can match that throughput.
Responsiveness Agents don’t wait for the next instruction—they sense shifts (like a sudden NPS drop) and act immediately.
Innovation By defining and pursuing sub-goals autonomously, agents can uncover strategies no one explicitly programmed.
In short, agency turbocharges AI’s business impact.
Agency vs. Automation
Automation: Simple, rule-based responses. Example: “If X happens, automatically do Y.”
Agentic AI systems are smarter, more adaptive, and capable of handling complexity without constant human oversight, unlocking far greater potential and value.
Quick Litmus Test
To determine if a system is genuinely agentic, ask yourself:
Does it define its own goal (or just follow mine)?
Can it plan multi-step workflows (or only run single scripts)?
Will it recover from unexpected issues (or simply error out)?
Will it learn from past mistakes (or repeat them tomorrow)?
All “yes” answers mean genuine agency. Anything less falls in at Levels 0–2.
Why a 0-to-5, 6-Point Scale?
Six levels strike the right balance between granularity and simplicity. They mirror familiar frameworks (like Likert scales in surveys), making adoption and communication smooth.
6-point Agentic Autonomy Scale
Level
Name
Core Capability
0
AI Tool
No initiative—human does everything.
1
Assistive Agent
Suggests next steps or drafts content; every action needs human approval. (Think AI Copilot) (Singh, 2025)
2
Partial Agency
Executes well-defined tasks end-to-end; humans still review exceptions.
Sets sub-goals, chains, multi-step workflows; rare human intervention.
5
Full Agency
Defines its own goals under guardrails; adapts to novel contexts; self-improves over time.
Sub-tiers of Level 3
Why break Level 3 into sub-tiers?
Level 3 covers everything from rigid, “follow-the-script” behavior to almost-fully autonomous workflows. By splitting it into five clear steps (3a–3e), teams get:
Concrete goals (“Move from 50% variation handling to 75% handling.”)
Visible progress (“We’ve improved from exact-pattern only to feedback-driven learning.”)
Focused priorities (Target just the next sub-level, rather than chasing full autonomy in one leap.)
Level 3 Sub-tiers of Agentic Autonomy
Level 3 Sub-tier
Name
Core Capability
3a
Exact-Pattern Execution
Executes only workflows that match a predefined pattern. Any deviation triggers immediate human intervention.
3b
Feedback-Driven Learning
Remembers how humans handled specific exceptions and applies those fixes automatically, with occasional review.
3c
Handles Common Variations
Successfully manages almost all sub-goal variations independently; only the most complex or sensitive cases require human intervention. For example, 50% variation handling.
3d
Handles Most Variations
Successfully manages almost all sub-goal variations independently; only the most complex or sensitive cases require human excalation. For example, 75% variation handling.
3e
Handles Nearly All Variations/td>
Rarely fails (<10% of cases). Requests human help only for truly novel or unforeseen tasks.
This structure turns a huge, fuzzy jump into manageable milestones—so you can measure real gains and plan the next enhancement with confidence.
Defining Agentic AI: A 6-Level Framework with Level 3 Nuance From AI Tool to Full Agency: The 6-Level Agentic Autonomy Scale
Real-World Mapping & Roadmap
Cast.app currently operates around Level 3a (3.0)—automating known workflows while handing novel cases back to humans. To climb to Level 3b, the roadmap includes:
Robust exception-handling: Reduce fallback rates by improving sub-goal recognition.
Dynamic policy updates: Empower agents to absorb new guidelines without manual retraining.
Feedback loops: Automate human corrections into self-learning routines.
Conclusion & Call to Action
The 6-Point Agentic Autonomy Scale offers a shared lens for evaluating AI maturity. Executives and investors can now easily categorize any system—from basic chatbots to sophisticated AI customer success managers—using this clear framework.
Where does your AI sit?
References
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics.
Sheridan, T. B., & Verplank, W. L. (1978). Human and Computer Control of Undersea Teleoperators. MIT Technical Report.
SAE International. (2014). SAE J3016: Taxonomy and Definitions for Terms Related to Driving Automation Systems.
