A Survival Guide to AI Agent & Agentic Risk

it's not you, it's our pricing structure

Congrats! You've just launched your AI agent into production. Everything runs fine. Your agent works flawlessly, your beta users are excited, and your investors/managers send congratulatory messages.

Then reality hits.

Your agent starts going rogue in ways you never imagined. It gets stuck in infinite loops pondering the nutritional benefits of stones. Your API costs suddenly look like a small country's GDP. And somewhere in Nebraska, your AI is telling customers it's actually a sentient penguin named Gerald (err Tay).

That’s when you realize that you fucked up. AI gone wrong has consequences.

In this post I don’t want to provide another dry risk assessment – I want to give you a survival guide through the treacherous waters of operational AI deployment.

The Risk areas that I am covering are:

1. Model Risk

2. Vendor Risk

3. Business Model Risk

4. Operational Risk

5. Data Risk

6. Compliance and Governance Risk

7. Security Risk

8. User Experience Risk

Let’s dive right in.

Model Risk

Model risk represents one of the most fundamental challenges in AI agent deployment. In short, it is the quality difference between models. Model Risk manifests in multiple ways that can severely impact your system's reliability and performance. I believe that the key challenge lies in effectively managing the implications of the unpredictable nature of model behaviors.

Your prompt might work on one model, but not on the other. Prompt engineering isn't universally transferable. A prompt optimized for GPT-4 might fail completely on Llama or Claude. This creates significant technical debt as you need separate prompt management systems for each model.

You don't have the changelog or milestones of model guardrails output changes. Model providers frequently update their systems without detailed documentation of behavioral changes. Today's perfectly functioning prompt could break tomorrow with no warning. You're often left debugging issues without knowing what changed underneath.

Fine-tuned model drifts from general-purpose performance. Fine-tuning can lead to unexpected degradation in non-targeted model capabilities. A model fine-tuned for financial analysis might suddenly perform worse at basic arithmetic or logical reasoning. This drift isn't always immediately apparent and can surface at critical moments.

Vendor Risk

Most frameworks use OpenAI as the default. In most use cases their model is the best and you can build solutions quickly. However, vendor dependency creates significant operational vulnerabilities in AI system deployment. The relationship between service providers and implementers is often asymmetrical, leaving businesses exposed to sudden changes in service conditions.

For example, what do you do if your API access gets banned for no reason? Sudden service termination can occur without warning or clear justification. API providers may flag normal usage patterns as suspicious or violate terms of service unintentionally. Recovery processes are often opaque and time-consuming, leaving systems non-functional during critical periods.

Your vendor increases prices destroying your unit economics. Price changes, like OpenAI Pro, can devastate carefully calculated business models. A 2x or 3x increase in API costs directly impacts margins, especially in high-volume applications. Small price adjustments compound significantly at scale, forcing rapid business model revisions. This also holds true for memory-as-a-service and tool-as-a-service providers.

Another vendor launches a model that has a better fit but you can't switch. Technical lock-in prevents rapid adaptation to market improvements. Competitors using newer, more efficient models gain significant advantages while you're stuck with legacy systems. Contract obligations and technical architecture can make switching prohibitively expensive even when better options exist.

Last-minute addition. Your AI vendor runs out of VC money, can’t keep talent, or is acquired by a larger player.

Business Model Risk

The economics of AI agent deployment often hide complex cost structures that can undermine business viability. Before deploying your agent in production it helps to understand the full cost implications and requires careful analysis of multiple interacting factors.

Giving your customer one price, but not understanding agent costs. Fixed pricing models often fail to account for variable usage patterns. Heavy users can generate exponentially higher costs through complex interactions and multiple API calls. Compared to traditional chatbots, reasoning models need to engage significantly more often with their “brain”. What seems profitable at a small scale can become unsustainable in real-world usage.

You ignored the cost of embedding context. NGL, that’s what happened to me with CrewAi. Embedding generation and storage creates hidden infrastructure costs. Each document or conversation requires vector embedding computation and storage. These costs scale linearly with data volume and can quickly exceed initial projections.

Your cloud-hosting costs exceed expectations. Large language models often require specialized hardware configurations. GPU instances cost significantly more than standard compute resources. Scaling requirements may force upgrades to more expensive infrastructure tiers. This is connected also to customer experience. If your agents need minutes to return a result, human users are likely not too happy about it. If your solution is successful, your cloud expenses can be expected to skyrocket.

Unexpected operational costs turn profitable models into liabilities. Hidden costs emerge in production environments. Error handling, redundancy systems, and monitoring tools add significant overhead not only during scaling. Support costs increase with system complexity and edge case handling.

Operational Risk

The day-to-day operation of AI systems presents unique challenges that can impact service reliability and user satisfaction. These risks often emerge only under real-world conditions.

Agents fail at edge cases you didn't anticipate. Production environments generate unexpected input variations. Users interact with systems in ways that test the boundaries of model capabilities.

Request: "I want to book a flight for my dog."
Agent Response: "Sorry, I can't find a flight for Mr. Dog in our database. Can you provide a different name?"

Edge cases can trigger cascade failures across dependent systems.

Agents loop infinitely over a problem without a conclusion. I observed this with my 4x4 playing Qwen agents. Recursive reasoning patterns can trap agents in circular logic. Without proper stopping conditions, agents consume resources without progress. Detection and intervention mechanisms add complexity to system design.

Latency issues from overloaded systems degrade experience. Performance degradation occurs under high load conditions. Response times increase unpredictably during peak usage. User experience suffers from inconsistent performance patterns.

Testing reliability under real-world load. Traditional testing methodologies often miss agent-specific failure modes. Load testing must account for model behavior variations under stress. Synthetic test data may not reflect real-world complexity.

Data Risk

Data quality is a common problem when building robust AI models and data quality and security fundamentally impact AI system performance and compliance. We all know that poor data management can compromise both system effectiveness and user trust.

Low-quality data cripples fine-tuning and prompting. Training data quality in dynamic few-shot prompting directly affects your model’s performance. Inconsistent or incorrect data produces unreliable results. Fine-tuning on poor examples reinforces undesired behaviors.

Sensitive data leakage in context or responses. Models can inadvertently expose private information — Especially if you work with memory. Context windows may contain residual sensitive data. Response generation might combine information from multiple sources inappropriately.

Your customer's data is shared with your vendor. Vendor terms often include data usage rights. Training data contributions may benefit competitors. Data sovereignty becomes unclear in multi-vendor scenarios.

Misalignment of synthetic training and real data. Synthetic data often fails to capture real-world complexity. Training-serving skew develops as real usage patterns diverge. Model performance degrades on actual user inputs.

Compliance and Governance Risk

Regulatory compliance, ISO 42001, presents unique challenges in agentic AI system deployment. As agentic workflows are still new, your internal governance frameworks must adapt to the rapidly evolving regulatory requirements while maintaining operational efficiency.

Here you should look out for:

Agents make decisions that conflict with regulations. Automated decisions may violate industry-specific rules. Compliance requirements change faster than model updates. Regulatory frameworks may not clearly address AI decision-making. As a general rule, you AI agents should never make a decision that would put a human in a worse situation. If uncertain always bring in a human.

Lack of auditable logs for decision-making. Traditional audit trails inadequately capture AI reasoning. Decision processes may be opaque or difficult to explain. Reconstruction of specific decisions becomes problematic. Some vendors offer access to the reasoning logs of your agents. Make sure you get access to them and learn how to read them.

Agents' reasoning lacks legal defensibility. Definitely important in regulated industries like financial advisory. Explanation mechanisms may not satisfy legal requirements. Complex decision chains create accountability challenges. Documentation standards for AI decisions remain unclear.

Security Risk

Security vulnerabilities in AI agent systems are underrepresented but present unique threats to system integrity and data protection. Traditional security measures may not adequately address Agent-specific risks.

Here are some common attack vectors:

Prompt injections compromise agent behavior. Malicious or malformed inputs can manipulate model responses. Security boundaries become unclear in conversation chains. Input sanitization challenges increase with model complexity. New tools give agents more capabilities to exploit guardrails.

Adversarial attacks generate harmful outputs. Carefully crafted inputs can bypass safety measures. Models may generate inappropriate or dangerous content. Attack surfaces expand with model capabilities.

Weak safeguards expose proprietary data. Security measures may fail under sophisticated attacks. Data extraction techniques evolve faster than protections. System compromises can leak valuable intellectual property. This point is especially important if you are using the system internally and inadvertently the system is exposed to the public Internet.

And finally!

User Experience Risk

User interaction with AI systems creates unique satisfaction challenges. Managing user expectations while delivering consistent value requires careful balance.

Agents act in ways that alienate customers. Your agents’ responses may lack appropriate context awareness. Tone and interaction patterns can feel unnatural or inappropriate. Users become frustrated with an apparent lack of understanding.

Overpromising capabilities lead to dissatisfaction: Marketing claims often exceed actual system capabilities. Users develop unrealistic expectations about AI performance. Trust erodes when system limitations become apparent.

I hope you found this meaningful to your implementation.

If you have any comments, please let me know.