AI Agent Governance

Posted by Marcus Bourke on Saturday, October 25. 2025

I wrote about AI agents back in February 2025. If you're still not sure what agents are all about, go back and read that article now. Since then, agents have evolved and become a lot more prevalent. Actually, agentic AI is all thre rage.

Agents are very powerful, but as the saying goes, with great power comes great responsibility. If you're deploying an AI agent, you must ensure that you have proper controls in place. That means implementing strong governance. Let's dig into that today.

Governing AI Agents in Production: How to Monitor, Audit & Correct Autonomous Behavior

So, we know that AI agents can act, plan, and take multi-step actions on behalf of users and systems. It doesn't take much imagination to see the potential risk that poses. Let's break down some practical ways to monitor, audit, and safeguard the agents that you deploy.

Why agents require different governance than static models

Traditional models respond to prompts. Agents act on their own. They call APIs, send emails, create records, move money (sometimes), and take multi-step actions that can really streamline business operations. Because these actions can have irreversible consequences, governance must move from “model QA” to product-grade operational controls:

Agents can compound errors over multiple steps.

Agents may act with delegated permissions that require careful boundaries.

Agent failures can create downstream business, legal, financial or safety incidents.

Rule of thumb: Treat every agent as if it were a small autonomous system. Design it for observability, implement safe defaults, and ensure fast undo/stop controls. Basically, treat it as a junior-level employee and follow the trust but verify model.

Core principles for agent governance

Design for observability. If you can't see what an agent did and why, you can't fix it.

Prefer constrained autonomy. Start with narrow, reversible actions and expand the agent's scope of control in a cautious, controlled manner.

Human-in-the-loop (HITL) by default for risky tasks. Humans should review important or irreversible actions until the agent proves itself. Then, humans should perform a random audit function.

Fail-safe first. Default to “do nothing” or “ask a human” when confidence is low in the agent's ability to complete the task successfully.

Auditability and explainability. Preserve decision trails that can be reconstructed later.

Monitoring: what to log and watch

Good monitoring is more than uptime. For agents, you need to monitor three key categories: actions, decisions, and effects. Below is a checklist of things to be able to monitor before a wide rollout

Essential logs

Action log: Record every API call, external interaction, message sent, or resource changed (timestamp, actor, context, target).

Decision trace: Save the reasoning or chain-of-thought summary used to choose the action (hashed or summarized for privacy where needed).

Inputs & outputs: Retain the prompt/state before the action and the response after the action (store this securely).

Confidence & provenance: Capture the confidence score, model version, data sources cited.

Rollbacks/compensating actions: Record when and why a rollback occurred.

Here's a possible JSON log format to get you started

{"timestamp": "2025-09-29T14:32:10Z",

"agent_id": "invoice_agent_v1",

"session_id": "sess-abc123",

"action": "create_invoice",

"target": { "account_id": "acct-789", "invoice_id": "inv-20250929-01" },

"decision_summary": "Extracted line items -> grouped by client -> generated invoice draft",

"confidence": 0.87,

"model_version": "gpt-xyz-1.2",

"sources": ["document_123", "contract_456"],

"outcome": "success",

"rollback": false}

Key metrics to tack and report

Task success rate (per-agent, per-task)

Rollback frequency (how often actions were reverted)

Escalation ratio (percent of actions flagged to humans)

Latency & cost per action

Anomaly rate (unexpected/unauthorized actions)

Auditing & explainability

How do you answer the question “why did it do that?” If you guessed audits, you'd be correct. Audits need to reconstruct the decision path. You won’t (and shouldn’t) log full chain-of-thought text in production for privacy and data volume reasons, but you must retain a compressed decision trail:

Decision IDs: Comprised of hashable references linking inputs to intermediate steps to the final action.

Source citations: Track the source for each claim or data point the agent used.

Snapshot storage: Keep snapshots of state for high-risk actions (e.g., financial transfers) for a defined retention window.

Periodic audits

Schedule recurring audits: weekly for high-risk agents, monthly for medium-risk, quarterly for low-risk. Use a combination of automated checks (pattern detection) and human review (sampled cases) to verify that the agent is in compliance.

Corrective mechanisms & safe defaults

Below are some practical controls to limit potential harm from a malfunctioning AI agent.

Global Kill Switch: Build a kill switch, or an immediate stop for all agent activity with a single command. Test it monthly.

Scoped kill Switches: Build in the ability to disable a specific agent or a class of actions (e.g., “no outbound emails”).

Permission Gates: Institute the requirement that the AI agent must request more privileged actions, which require human approval.

Sandbox mode: Create and environment to allow agents to simulate actions and produce “what would happen” reports before doing the real thing.

Compensating transactions: For reversible domains, create automated rollback flows, such as the ability to cancel an invoice, process a refund, or reverse updates.

Critical Step: Implement a two-step commit process for irreversible actions. For example, the agent posts a proposed change and a human, or a timed automatic condition, confirms it.

Governance structures & organizational roles

Who's accountable for AI agent safety? Effective governance requires a defined governance model, named roles and responsibilities (ideally in the form of a RACI diagram), and an incident lifecycle.

Suggested roles

Safety Owner: Product or engineering lead accountable for day-to-day safety and incident triage.

Agent Review Board: Committee of cross-functional reviewers (product, engineering, legal, security) for major agent launches, permission upgrades and audit reviews/approvals.

Compliance Liaison: Owns audit readiness, reporting to the Agent Review Board and any required external reporting.

On-call Incident Responder: First responder responsible for handling immediate mitigation (activating kill switches, rollbacks, etc.).

Incident Resolution lifecycle (high-level)

Incident Detection (automated monitoring or customer reported)

Triage the incident to assess the incident and impact (Safety Owner + on-call first responder)

Mitigate this incident (activate kill switch, revoke permissions, execute a rollback, etc.)

Lessons Learned session to ensure it doesn't happen again (post-mortem and root cause analysis)

Remediate the root cause & document appropriately (bugs fixed, controls updated, etc.)

Communicate transparently about the issue, impact and resolution (customers, internal stakeholders, regulators as required)

Deployment strategies: How to roll agents out safely

Plan for a phased rollout to minimize collateral damage.

Parallel mode

Run an agent in parallel (observation-only) to compare proposed actions against business rules without actually executing them. This is critical for validating agent behavior under production-like conditions before going live.

Canary or pilot releases

Allow the agent to operate for a small group of users or accounts. Monitor metrics closely and expand only if the results are as expected and the agent is operating safely.

Phase in elevated permissions

Start with read-only access, then assign incremental permissions (write drafts, submit for approval, execute) as the agent proves itself. Each permission increase must be reviewed and approved by the Agent Review Board, monitored and periodically audited.

Examples of a rollout schedule

Phase	Duration	Allowable Actions
Parallel	2–4 weeks	Observe & log only
Pilot	1–2 weeks	Execute low-risk actions for a sample of customer/accounts
Pilot	2–6 weeks	Execute broader actions with human approval
Production Rollout	Ongoing	Full permissions with monitoring & periodic audits

A hypothetical case study

A short fictitious example to illustrate governance in practice.

Imagine an invoice agent that drafts invoices from contracts and submits them to customers. In production it mistakenly billed a test account because a flag in the sandbox environment was unset. With governance in place the team:

Detected unusual billing via anomaly monitors (surge in invoices for test accounts).

Triggered the scoped kill switch to stop additional invoice generation.

Rolled back the erroneous invoices using automated compensating actions.

Ran a post-mortem and determined that the root cause was environment misconfiguration. Remediation called for an additional gate check and guardrails in the agent planner.

Published a customer-facing incident report and updated the risk register.

The end result: quick remediation, minimal customer impact, and improvements that made the agent safer.

Potential operational checklist for agent governance

Agent Governance Checklist

1. Observability

   - Action logs enabled

   - Decision traces linked to actions

   - Confidence & model-version metadata

2. Monitoring

   - Task success rate dashboard

   - Rollback & escalation metrics

   - Anomaly detection on actions

3. Safeguards

   - Global kill switch tested

   - Scoped kill switches available

   - Permission gates for privileged actions

4. Auditing

   - Weekly sample audits (high-risk agents)

   - Quarterly full audits

5. Roles & governance

   - Named Safety Owner

   - Agent Review Board charter

   - Incident runbook + post-mortem template

6. Rollout

   - Parallel -> Canary -> Pilot -> Full Production plan

7. Communication

   - Customer incident template ready

   - Internal escalation contacts documented

Abbreviated post-mortem template

Incident Post-Mortem

1. Title & date

2. Incident summary (1-2 sentences)

3. Timeline of events (concise)

4. Root cause

5. Impact Assessment (users, data, financial)

6. Immediate mitigation steps

7. Root cause fixes & owners (with deadlines)

8. Preventive measures & monitoring updates

9. Customer communications & compensation (if any)

10. Lessons learned

Final thoughts & next steps

Running AI agents in production raises the bar on the need for governance, but it’s also solvable with engineering discipline, thoughtful product design, and clear organizational ownership. Start small, expand in a controlled manner, and treat safety as a critical function, the same way you treat performance and reliability.

Immediate actions you can take today: enable action logging, define a Safety Owner, and add a “parallel mode” for your highest-risk agent(s). Those three moves drastically reduce collateral damage and buy you the time needed to build a robust goverenance model and implement associated controls.

Interested in working with us? Check out FailingCompany.com to learn more. Go sign up for an account or log in to your existing account.

#FailingCompany.com #SaveMyFailingCompany #ArtificialIntelligence #AIAgentGovernance #SaveMyBusiness #GetBusinessHelp

AI Startup Myths

Posted by Marcus Bourke on Saturday, October 18. 2025

Hopefully you're well on your way with your AI start up by now. Last week's post should have helped you down the right path to gain some real traction in your business. But what other issues do you need to know about? Are there any land mines to watch our for that could sink your business? With something as hot as AI, you already know the answer to that. Let's check it out today.

AI Startup Myths That Could Sink Your Business (And What to Focus on Instead)

It seems like you can't go anywhere without hearing about AI...in the news, on social media, in the boardroom, and in just about every other corner of the planet. Unfortunately, that means that there are plenty of myths floating around as well. If you’re building an AI startup, buying into these myths can torpedo your business. Let’s tackle some of the biggest myths and talk about what you should focus on instead.

Myth #1: “If You Build Amazing AI, Customers Will Come”

This is the classic “Field of Dreams” trap. Founders assume that if they train the most advanced model, customers will line up at the door. The truth is that most customers don’t care about your algorithm. They care about what problem you solve for them and how it impacts their bottom line.

Reality: Successful AI startups like Gong and Jasper thrived not because they had the “best” models, but because they solved urgent pain points (sales insights, content creation) and packaged them in easy-to-use products.

Focus instead: Don't deviate from solid business fundamentals. So, always lead with customer value. Translate your AI solution into clear outcomes like time savings, increased revenue, operating cost reduction or risk reduction. Let the tech stay behind the scenes, and let business outcomes be the trailer for the feature film.

Myth #2: “More Data Automatically Means Better AI”

It’s easy to assume that adding in more data will magically make your AI smarter...and more competitive. But data without quality, diversity, or proper labeling can backfire on you. It can end up producing biased, noisy, or even dangerous outputs. That will be of no benefit to your business.

Reality: Startups like Scale AI built their business not around “more data,” but around better data. They invested in clean, structured, and high-quality inputs that made their AI systems usable and beneficial in the real world.

Focus instead: Curate data ruthlessly. Spend energy on quality datasets, feedback loops, and continuous improvement rather than training your models on terabytes of potentially junk data.

Myth #3: “Big Models Always Win”

There’s a myth that the path to success is building the biggest, most complex models possible. But training massive models is expensive, risky, and rarely practical for startups. You can’t outspend OpenAI or Google.

Reality: Many thriving startups (like Runway and Perplexity) succeed with smaller, fine-tuned, or specialized models that do one thing incredibly well.

Focus instead: Find niches where smaller, more efficient models shine. Customers care about accuracy, speed, and usability. If using a model adds clear business value, then they aren't going to care about the parameter count of your model.

Myth #4: “Riding the Wave of AI Hype Is Enough to Attract Investors”

In 2021, this myth almost seemed true. Money poured into anything with “AI” in the pitch deck. But now, the market feels saturated and investors have gotten more discerning. They’ve seen too many flashy pitches that never turned into revenue to continue to throw money at every "AI" opportunity.

Reality: Funding has shifted toward startups with traction, not just cool technology or POCs. Investors want to see paying customers, proof of ROI, and a path to scale. Even buzzy startups like Adept AI have faced tough funding rounds because hype alone doesn’t pay the bills.

Focus instead: Build traction before chasing big investors. Focus on the fundamentals by nailing customer validation, proving ROI, and showing a repeatable sales model. Then funding just becomes fuel to keep moving down the road.

Myth #5: “AI Will Replace the Humans (So Customers Won’t Need Staff)”

Founders sometimes oversell AI as a total replacement for human roles. That’s not just misleading, it can be a trust killer if it's not definitively true. Customers don’t want to fire entire teams unless they have an immediate need to significantly reduce admin cost. Rather, they want tools that augment their people and make them more productive.

Reality: Startups like UiPath succeeded by positioning AI as a “digital assistant” that helps workers get rid of repetitive tasks. That narrative won trust and adoption.

Focus instead: Frame your AI as augmenting humans, not replacing them. Show how it makes employees smarter, faster, or more effective. That’s a message customers can embrace without fear. It's also a message that their employees can embrace, increasing the odds of a successful implementation.

Myth #6: “Ethics and Compliance Can Wait Until Later”

Startups often push responsible AI to the back burner, figuring they’ll fix it once they get bigger. Big mistake. Issues like bias, privacy, and transparency can kill deals early if enterprise customers sense risk.

Reality: Companies like Anthropic have built their entire brand around responsible AI...and it’s winning them major enterprise contracts.

Focus instead: Bake ethics, privacy, and transparency into your company DNA from day one. Clear model cards, explainable results, and thoughtful data policies aren’t just compliance...they’re competitive advantages.

Myth #7: “You Have to Go Broad to Succeed”

Some founders try to build AI that can solve everything for everyone. That’s a fast track to confusion and value dilution.

Reality: The most successful AI startups almost always start narrow. DeepL didn’t try to “do all AI." Instead, they nailed translation. PathAI focused on pathology before expanding. Specialization builds credibility, customers, and traction.

Focus instead: No company, AI or not, can be everything to everyone. Pick one pain point, solve it exceptionally well, and then expand once you’ve earned trust and revenue.

Let's Recap What You Should Actually Focus On

Strip away the myths and the playbook becomes clearer. Focus on business fundamentals:

Customer pain points first. Solve urgent problems, not just interesting ones.

Quality over quantity in data. Curated datasets beat massive ones.

Practical AI. Choose speed, usability, and ROI over chasing the biggest model size that can do everything.

Responsible AI. Make ethics and compliance part of your company's DNA, not an afterthought.

Start narrow. Dominate one use case before expanding. Then look for complimentary problems to address.

Final Thought

AI is still one of the most exciting places to build right now. But the graveyard of failed AI startups is filling up quickly. They all had brilliant ideas but believed in the wrong myths. If you stay grounded in the business fundamentals, customer-focused, and ethics-driven, you’ll put yourself in the small but powerful group of AI startups that not only survive, but thrive.

Building Traction With Your AI Startup

Posted by Marcus Bourke on Saturday, October 11. 2025

If you read last week's article, then you have a good idea on how to build trust with your AI startup. So, what comes next? Well, how do you actually get traction and grow your business. We don't want you stuck in the pilot phase for ever. So, let's explore some ways to build traction this week.

Moving From Pilot to Sustainable Profit: How AI Startups Can Win Their First Real Customers

We've talked extensively about how AI startups are popping up everywhere. We've also talked about how most never make it past the pilot stage. Here are some ways to avoid “pilot purgatory” and start building real customer traction.

Why So Many AI Startups Get Stuck in Pilot Purgatory

Pilots can be a double-edged sword. On one hand, they’re a great way to test a product in the real world with lower risk. On the other, they often stall for predictable reasons:

No clear success metrics. Without defined outcomes, it’s hard to prove a pilot was worth paying for.

Solving the wrong problem. Flashy AI tricks don’t matter if they don’t address a core pain point.

AI curiosity, not commitment. Some companies just want to “check the AI box.”

Integration headaches. A standalone pilot may break down in real workflows and systems.

Too broad a focus. If your AI “does everything,” customers may not know what you actually solve.

Lessons from AI Startups That Escaped Pilot Purgatory

1. Hugging Face: Build a Community Before the Customers

We've talked about Hugging Face in past articles. It started as a chatbot app but pivoted when they saw demand for open-source AI tools. By fostering a developer-first community, they built credibility and adoption before monetizing.

Takeaway: Sometimes your first “customers” are users and developers who expand your reach.

2. Scale AI: Solve Painfully Specific Problems

Scale AI tackled a very specific problem, which was labeling training data. Their narrow focus won contracts with OpenAI, Cruise, and others.

Takeaway: Pick a specific problem that’s urgent and critical, and become the very best at solving that poblem. Launch a pilot with a clear plan to scale.

3. DataRobot: Sell ROI, Not Tech

DataRobot emphasized cost savings and faster predictions, not algorithms. Essentially, they focused on delivering clear business value and their ROI-driven messaging helped close deals.

Takeaway: Customers buy outcomes, not technology. Show the financial impact or some other way to deliver real business value to stand out from your competitors.

4. Gong: Build Insights Into the Workflow

Gong didn’t just analyze call transcripts, rather they delivered insights directly into sales managers’ workflows. This made adoption seamless, addressing a common barrier to new technology adoption.

Takeaway: Package insights so they fit naturally into the customer’s workflow, lowering the barrier for new technology adoption.

How do You Turn Pilots Into Paying Customers?

If you’re an AI founder worried about getting stuck in the pilot phase, then here are some steps to convert your experiment into recurring revenue:

Step 1: Choose Pilots Carefully

Ask: Does the company have budget authority? Is the problem urgent and tied to money or risk? Can impact be measured in 60–90 days?

Step 2: Define Success Metrics Upfront

Agree on adoption, accuracy, and ROI goals at the start and put them in the pilot agreement.

Step 3: Price for Commitment

Free pilots often go nowhere. Even small fees give customers skin in the game. Use tiered pricing to filter out “tire kickers.”

Step 4: Integrate Early

Don’t isolate your pilot. Integrate into workflows or systems from day one for higher adoption.

Step 5: Show Quick Wins

Design pilots to deliver visible results in 30–60 days to build momentum and executive support.

Step 6: Turn Champions Into Evangelists

Empower internal champions with dashboards, case studies, and wins they can brag about.

Step 7: Document and Scale

Each successful pilot should generate case studies, testimonials, and ROI data you can be used to drive future sales.

The Mindset Shift: From Producing Cool Tech to Becoming a Trusted Partner

The startups that thrive don’t just show off flashy AI toys. Rather, they solve real difficult problems, deliver measurable ROI, and fit into common workflows. They become partners, not vendors.

Remember, AI can be fun and exciting, but customers don’t buy excitement. They buy results.

Final Thought

Breaking out of pilot purgatory is the defining challenge for AI startups. But you can treat pilot as a springboard instead of the end result by choosing wisely, pricing smartly, and proving value. You’ll soon build traction that hype can’t deliver.

Because in the end, the AI startups that thrive aren’t the ones with the fanciest models. They’re the ones with the happiest customers.

Build Trust With Your AI Startup

Posted by Marcus Bourke on Saturday, October 4. 2025

Well, the past few weeks have unpacked the reasons why so many AI startups fail, what you can do to beat the odds and have even put together a survival guide. What could be next?

We know AI startups are all the rage. We also know that for every success story like OpenAI or Anthropic, there are dozens of AI startups that quietly vanish. The number one factor that separates survivors from failures? Trust. So, that's what's next this week. Let's talk about building trust.

Building an AI Startup That Investors and Customers Actually Trust

In a world flooded with overhyped promises and half-baked AI products, winning (and keeping) the trust of investors, customers, and end-users isn't just a good idea. It's the secret sauce. Let’s dig into some practical steps, real-world examples, and some templates you can start using to build trust with your customers and investors.

Why Does Trust Matter?

AI startups often overpromise, underdeliver, or hide key details about how their technology actually works. Customers and investors don’t just want cutting-edge models...they want transparency, reliability, and accountability. Without those, even the coolest AI demo won’t last long in the real world.

Case in point: Babylon Health, once valued at $4B, collapsed after questions arose about the accuracy and safety of its AI-powered medical claims. The tech itself wasn’t the demise. It was the lack of trust that killed the company.

Compare that with Anthropic or Perplexity AI, who lead with transparency and safety. They not only push “smarter” AI, but they emphasize guardrails, explainability, and ethical use. That’s what builds credibility and trust.

How to Build Trust: A Playbook for AI Startups

Here are some hey ways to build lasting trust with your AI startup.

1. Publish Trust Artifacts

Don’t just say you’re transparent. Every startup can do that. Remember, actions speak louder than words. Publish documents that spell out how your AI works, what it can and cannot do, and how you handle data. Then, do exactly what you say you're doing in those documents.

Model Card:

Include model name & version, release date, training-data summary, intended use cases, evaluation metrics, known limitations, and a support contact. See below for an example:

Model: Acme-Summarizer v1.0 (released 2025-08-01)
Trained on: Mix of public web data + anonymized customer docs
Intended use: Summarizing business text
Not for: Medical, legal, or safety-critical advice
Primary metrics: ROUGE-L 45, factuality 92% (sampled)
Known limits: May omit key facts; verify critical outputs

Datasheet for Datasets: Summarize sources, sampling, cleaning, and bias checks.

"What We Can’t Do Yet" Page: Openly and honestly list the limits of your AI product.

We do not provide medical diagnoses. Use our suggestions as drafts, not final decisions.

Security & Compliance Summary: List encryption, audits, and compliance status.

2. Use Operational Checklists

Checklists keep you honest and prevent oversight. Start with these three:

Data Governance Checklist

Inventory: what data you have, where it lives, who has access

Retention & deletion policy

Consent tracking for customer data

Anonymization / minimization steps

Immutable logs for dataset updates

Security Checklist

TLS + encryption at rest

Role-based access control (RBAC)

Secrets management

Automated backup + tested restore

Incident response runbook

Compliance Checklist

Data Protection Impact Assessment (DPIA) if handling personal data (GDPR)

Map requirements for SOC 2, HIPAA, ISO27001 as needed

3. Run Pilots That Prove Value

Pilots build trust when they’re structured. Consider using this four-phase approach:

Discovery: Map data, define success metrics

MVP: Deliver a working feature for small user group

Pilot: Limited production use with metrics tracking

Evaluate & Scale: Decide go/no-go with customer

Create Clear Pilot Success Criteria

Adoption: % of users using weekly

Accuracy: % of outputs verified correct

ROI: measurable savings or revenue lift

Safety: zero critical incidents

4. Test and Monitor Relentlessly

Trust grows when customers know you’re always testing and looking for issues or vulnerabilities. Here’s one way to do that:

Red Teaming: Stress-test your model quarterly

Human Sampling: Audit 1–2% of outputs

Monitors: Track uptime, cost, hallucination rate

Rollback Criteria: Predefine thresholds for disabling features or rolling back to a previous version

5. Track Trust Metrics

You can't just assume that you're building trust. You also can't guess at how well you're doing. You must measure it.

Quality: Accuracy, hallucination rate

Usage: Retention, adoption, daily & weekly active users

Business: Customer Churn, Net Revenue Retention (NRR), Lifetime Value (LTV) and Customer Acquisition Cost (CAC)

Support: Customer Issue Escalations, resolution time

Security: Incidents, audit findings

6. Communicate Transparently

Clear communication is half the battle.

Pre-Launch

Publish FAQs, model cards, and limitations upfront.

In-Product Disclaimers and Guidance

This content was generated by Acme AI. It may omit details. Click "Show Sources" to verify.

Incident Response Template

Timeline: what happened & when

Root cause

Impact

Mitigations

Preventive actions

7. Build Trust Into Your UX

Explain This Button: Show sources or reasoning

Confidence Scores: Simple ranges, not magic numbers

Feedback Loop: Easy reporting of bad outputs

Data Controls: Clear opt-outs for training data

8. Formalize Governance

Assign a Safety Owner

Create an external Ethics Board (if working in a regulated domain)

Conduct regular third-party audits

Align contracts & SLAs with reality

Key Takeaways

Building an AI startup that people actually trust isn’t about showing off the smartest model. It's not about the wow factor. It’s about making your work transparent, reliable, and accountable from day one and never deviating from that philosophy.

Publish trust artifacts

Run disciplined pilots

Track trust metrics

Communicate openly (especially when things go wrong)

Embed trust in product design and governance

Do this, and you won’t just avoid the AI startup graveyard, you’ll stand out from the crowd. Because in the long run, trust beats buzz every time.