I recently published The Agentic AI Governance Framework, and in this article, I'll discuss why it matters and how you can use it. Agentic AI systems make decisions on their own. They act in the real world. They call APIs, move money, control robots, and sometimes break things. As these agents grow more powerful, we need rules that keep them safe, fair, and accountable. That is what this framework tries to address. I created this framework to give teams a straightforward way to manage risk in autonomous AI and to distill the key insights from the paper: • It outlines six core principles. • It introduces a new metric: the Agentic Log Retention Index (ALRI). • It details how to implement audit controls in tools like LangChain, AutoGen, CrewAI, and Microsoft Semantic Kernel. You can read the full report for free on Zenodo (CERN-backed). Feel free to cite it here: https://doi.org/10.5281/zenodo.17426620 https://doi.org/10.5281/zenodo.17426620 Why This Framework Exists Most AI governance talks about models; this one talks about agents. An agent is not just a language model; it is a loop of observation, thinking, acting, and repeating. Each loop can change the world. If something goes wrong, you must know why and when. Regulators are paying attention, too. The EU AI Act goes into effect in 2026, and high-risk systems will need proper logs, risk tiers, and real human oversight. The problem is, teams using agents today don’t have a clear or consistent way to prove they’re compliant. This framework is designed to close that gap. The Framework's Six Principles 1. Complete Traceability: Every decision gets a full log. Input, reasoning, action, and result. No gaps. Complete Traceability: 2. Risk-Based Controls: Low-risk agents need light checks. High-risk agents need full audit trails and approvals. Risk-Based Controls: 3. Tamper-Proof Storage: Logs use digital signatures. No one can edit history after the fact. Tamper-Proof Storage: 4. Human in the Loop: Critical actions pause for review. The system flags high-impact choices. Human in the Loop: 5. Continuous Monitoring: Live dashboards show agent health, drift, and error rates. Continuous Monitoring: 6. Clear Accountability: Every agent has an owner. Every action ties back to a person or team. Clear Accountability: The ALRI Score:  How to Measure Log Quality Words are easy, but numbers force honesty. The Agentic Log Retention Index or ALRI gives a score from 0 to 1. Higher is better. To move from theory to practice, I defined a single metric that forces teams to quantify how accountable their agents really are. ALRI = sum (weight × completeness) across all log fields ALRI = sum (weight × completeness) across all log fields Duration weight: how long logs stay Duration weight: how long logs stay Completeness weight: are all steps recorded Completeness weight: are all steps recorded Tamper evidence weight: are logs signed and hashed Tamper evidence weight: are logs signed and hashed Example: An agent with 90-day retention (0.3), full step logging (0.35), and SHA-256 signatures (0.25) scores 0.90 — meeting high-risk compliance. The actual paper has detailed examples. Also, these are for archival logs. Example: A score above 0.85 meets high-risk rules. Below 0.60 means redo your setup. A score above 0.85 meets high-risk rules. Below 0.60 means redo your setup. Real Code You Can Copy The report has ready code. Here is one example for LangChain in Python: Here is one example for LangChain in Python: # Example: Signed logging in LangChain

from langchain.callbacks import get_openai_callback
import hashlib
import json
import time

def signed_log(chain, input_text, output_text):
    timestamp = time.time()
    entry = {
        "input": input_text,
        "output": output_text,
        "timestamp": timestamp,
        "chain_id": id(chain)
    }
    payload = json.dumps(entry, sort_keys=True)
    signature = hashlib.sha256(payload.encode()).hexdigest()
    entry["signature"] = signature
    with open("agent_log.jsonl", "a") as f:
        f.write(json.dumps(entry) + "\n")
    return entry

# wrap your chain
with get_openai_callback() as cb:
    result = chain.run("plan next step")
    signed_log(chain, "plan next step", result) # Example: Signed logging in LangChain

from langchain.callbacks import get_openai_callback
import hashlib
import json
import time

def signed_log(chain, input_text, output_text):
    timestamp = time.time()
    entry = {
        "input": input_text,
        "output": output_text,
        "timestamp": timestamp,
        "chain_id": id(chain)
    }
    payload = json.dumps(entry, sort_keys=True)
    signature = hashlib.sha256(payload.encode()).hexdigest()
    entry["signature"] = signature
    with open("agent_log.jsonl", "a") as f:
        f.write(json.dumps(entry) + "\n")
    return entry

# wrap your chain
with get_openai_callback() as cb:
    result = chain.run("plan next step")
    signed_log(chain, "plan next step", result) Plug this in, and every run writes a signed line. Auditors love it. Who Should Use This Framework - Engineering leads building multi-agent teams - Compliance officers preparing for EU AI Act audits - Startup founders who want trust from enterprise clients - Researchers testing new agent designs It works with any stack. The tables in the report map each principle to five platforms. Pick your tool, copy the pattern, and you're done. If you build, deploy, or audit autonomous systems, this is for you. What I Learned Writing It I built three small pilot systems to test the ideas. One agent booked travel. One traded mock stocks. One is the triage of incident tickets. The logs showed where plans failed fast. ALRI caught missing fields in hours, not weeks. I also saw limits. Agents that phone home to external APIs sometimes lose context. Parallel actions create race conditions in logs. The framework flags these as open issues. Version two will not only add patterns for them but also come with a framework for the developers. This is version 1.0. It is open under CC BY 4.0. Fork it, improve it, break it. The goal is a standard everyone can use before regulators write one for us. Let us make agentic AI safe by design, not by accident. Download the full open-access paper here: [Agentic Ai Framework - Zenodo (CERN Sponsored) ] and join the discussion; every implementation idea helps refine version 2. Download [ Agentic Ai Framework - Zenodo (CERN Sponsored) Agentic Ai Framework - Zenodo (CERN Sponsored) ]

This story contains new, firsthand information uncovered by the writer.

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