Tags
Agentic Systems, AI Maturity, Governance
Author
Wout Helsmoortel
Every organization begins its AI journey at a different point, but the progression always follows a similar path. Systems first automate structured tasks, then assist with reasoning, and eventually collaborate through agentic behavior. This evolution is not only technical; it reshapes how teams design, evaluate, and govern intelligence. Each level builds on the previous one, adding layers of reasoning, visibility, and shared control.
Level 1: Automation
What it is
Software executes fixed rules on structured inputs. Every path is predefined, with no model reasoning, only scripts and business logic.
Why it is useful
Automation removes repetitive work and standardizes tasks. It’s easy to audit because behavior is predictable, but that same rigidity makes it brittle when conditions change.
Typical stack
RPA (Robotic Process Automation) tools or workflow engines that run deterministic rules through well-scoped APIs and basic monitoring.
Risks and limits
Automated processes break when inputs drift or policies change. They have little tolerance for ambiguity and don’t scale well to tasks requiring judgment or interpretation.
Example
An enterprise uses a rules engine to prefill a Risk Assessment Brief (RAB) from a system inventory. It inserts the system name, owner, and last scan date, then routes the incomplete sections to a human reviewer. The process runs quickly but cannot rate risks or propose mitigations.
How to advance
Introduce retrieval of relevant documents and start evaluating outputs against simple checks, such as completeness or required sections. Add basic traceability so it’s clear what information was used to generate each draft, and align this with your governance baseline.
Level 2: AI Workflows
What it is
At this stage, models are added to the process. The system can retrieve information, draft content, classify items, and suggest answers. The overall flow remains mostly predefined, but parts of the work are learned rather than scripted.
Why it is useful
AI workflows create a large productivity leap for research- or document-heavy tasks. Teams receive more complete first drafts and faster triage, while humans still review, approve, and refine.
Typical stack
Retrieval-augmented generation (RAG) over approved sources, supported by prompt libraries, deterministic glue code, and observability that logs prompts, sources, and outputs. Evaluations guard against regression and drift.
Risks and limits
Reasoning remains hidden unless you add tracing. Quality depends heavily on the sources and prompt design, and without ongoing evaluation, degradation can go unnoticed.
Example
In preparing a Risk Assessment Brief (RAB), an analyst asks the system to “draft risks for the new payroll integration.” The workflow pulls the risk template, retrieves past cases and policy clauses, and drafts five candidate risks with likelihood and impact scores, each with a citation. The reviewer edits and approves the result. The process is still scripted, but the content is now model-generated.
How to advance
Make the system explain how it reached its conclusions and learn from review feedback. Add evaluator models that check for correctness, groundedness, and policy adherence. Compare human and model judgments regularly to calibrate performance.
Level 3: Agentic Systems
What it is
At the final stage, the system can plan, select tools, act, and self-check within defined guardrails. It adapts to context, collaborates with humans, and continuously improves through an evaluation loop. Multiple agents can coordinate—for example, a drafting agent supported by an evaluator agent.
Why it is useful
Performance scales with task complexity. The system can surface uncertainty, ask targeted questions, and route ambiguous cases to humans. It also becomes easier to trace and explain decisions, building trust in its outputs.
Typical stack
Dynamic orchestration for planning and control flow, tool calling, memory, and role-specific policies. Evaluator agents score outputs using expert rubrics. Observability captures prompts, reasoning steps, actions, and reviews, while governance documents how the system behaves and evolves.
Risks and limits
With more moving parts comes greater oversight needs. Evaluators themselves can be biased if not tested and maintained. Third-party or independent evaluations help keep quality in check.
Example
When producing a Risk Assessment Brief, a drafting agent plans the analysis, retrieves relevant threat references, and proposes risks. An evaluator agent checks completeness, adherence to internal scoring rules, and citation quality. Any disagreement between agent and reviewer is logged and used to refine rubrics and prompts. Over time, routine risks are auto-approved, while novel or high-impact ones remain for human review.
How to climb the ladder
Codify quality
Define what “good” looks like before you start building. Create explicit rubrics that measure completeness, correctness, and clarity.Govern sources
Use trusted repositories and documented provenance. Every system must know which data is official, current, and approved.Add tracing
Record prompts, retrievals, reasoning, and reviews. Allow replay for audits and failure analysis.Evaluate continuously
Compare human and model outputs. Measure consistency, bias, and error recovery over time.Keep humans in command
Clarify who owns each decision stage. Escalate exceptions automatically but ensure final accountability stays human.Expand incrementally
Move one level at a time. Automation maturity supports AI workflows; strong workflows make agentic systems sustainable.
Conclusion
The path from Automation to AI Workflows to Agentic Systems is not about replacing people with technology. It is about designing systems that extend human reasoning while preserving control and accountability.
Automation gives structure and speed. AI Workflows introduce context and interpretation. Agentic Systems close the loop, combining autonomy with transparent evaluation.
Progress along this ladder requires more than technical upgrades. It demands better data governance, clearer evaluation standards, and continuous human oversight. Organizations that invest in these foundations build systems that not only act intelligently but also explain, adapt, and improve, turning automation into genuine, auditable intelligence.
References and Acknowledgments
Author: Wout Helsmoortel
Founder, Shaped — specializing in agentic AI systems, explainable architectures, and learning transformation for defense and enterprise environments.
Academic references
NIST. “AI Risk Management Framework 1.0.” NIST
ISO/IEC. “ISO/IEC 42001: AI management systems.” ISO
OpenAI. “Working with evals.” Developer guide. OpenAI Platform
OpenAI. “Getting started with OpenAI Evals.” OpenAI Cookbook
Anthropic. “A new initiative for developing third-party model evaluations.” Anthropic
LangChain. “Workflows and agents,” LangGraph docs. LangChain Docs
IBM. “AI Factsheets.” IBM
Gu et al. “A Survey on LLM-as-a-Judge.” arXiv
Li et al. “LLMs-as-Judges: A Comprehensive Survey.” arXiv
