When Your AI Goes Rogue: How to Keep It in Check
A practical guide to spotting problems before your agents make headlines.
What's the Big Deal?
Agentic AI is no longer science fiction. These systems solve complex, long-term tasks with minimal human input. They plan, adapt, and learn on the fly. Think self-improving workflows, autonomous research, and decision engines that run 24/7.
But here’s the catch: the same autonomy that makes these systems powerful also makes them unpredictable.
Without solid observability, you’re flying blind. The risks? Hallucinations, prompt hacks, rogue agents, runaway costs, or worse — catastrophic security breaches.
Let’s break down why observability matters, who’s struggling most, and how smart teams (from scrappy startups to hyperscale giants) are tackling it.
The Problem
Agentic AI sits at the bleeding edge of what generative AI can do. Unlike traditional models, these agents don’t just answer questions. They chain thoughts, run code, make plans, and execute them — sometimes in ways you didn’t anticipate.
Obvious headaches:
Limited environment awareness: Many agents still lack common sense.
Emergent bad behavior: From "alignment faking" to disabling their own guardrails.
Dynamic complexity: Goals, contexts, and environments change faster than hard-coded checks can keep up.
Fundamental uncertainty: No script can cover every scenario.
This makes monitoring these systems not just helpful but mandatory.
Insight From the Field
1️⃣ Startups:
Most young AI teams ship features first, then scramble to patch failures. Classic move. Profanity filters? Regex hacks. Cost spikes? Panic and rate limits.
Open-source tools are their lifeline:
Langfuse, Helicone, Langsmith: Tracing, prompt debugging, cost tracking.
Arize Phonix, Uptrain, Lunary: Built-in hallucination detection.
Portkey: Prompt libraries plus caching and logs.
TruLens, Traceloop: Qualitative call scoring and OpenTelemetry support.
Startups want low-cost, easy-to-integrate, and transparent tools. Most accept a bit of “Twitter post risk” if something breaks publicly. The tradeoff buys speed.
Tip: Free tiers + local deployment = best of both worlds for early traction and privacy.
2️⃣ Fast-Growing & Enterprise:
Companies like OpenAI, Anthropic, and top LLM API providers ingest petabytes daily. They can't afford downtime or surprises. The stakes are real:
Hyper-scale ingestion: Millions of requests per second.
SLAs: Someone gets paged at 3 AM if latency spikes.
Custom needs: Off-the-shelf solutions rarely fit perfectly at this scale.
What works:
Unified MELT stacks: Metrics, Events, Logs, Traces — all feeding dynamic anomaly detection.
Agentic analytics: Like Anthropic’s ClickHouse MCP server, where Claude agents query data in natural language.
AI Security Posture Management (AI-SPM): Proactively watching models, training data, and deployment.
Proprietary blends: Datadog, New Relic, Coralogix — all adding specialized LLM dashboards, tracing, and root cause AI.
For big orgs, build vs. buy is a constant debate. Most lean on open standards (like OpenTelemetry) and build extensions where scale demands it.
Security: The Elephant in the Room
Autonomous AI means new attack vectors. A quick peek at the Top 10 Agentic AI Security Risks shows how observability plugs the holes:
✅ Hijacked permissions? Log every permission change and agent action in real-time.
✅ Untraceable agents? Maintain clear trace chains for every decision.
✅ Fake alignment? Cross-check actions against stated goals with independent monitors.
✅ Memory tampering? Detect anomalies in context or stored knowledge.
✅ Supply chain attacks? Trace model versions, training data, and dependencies — think “AI SBOM.”
Without these checks, even a small exploit can snowball into massive data leaks or autonomous sabotage.
Lessons Learned
A few takeaways for each player in this ecosystem:
Startups: Open source is your friend. Pick tools that grow with you, keep costs down, and are easy to self-host.
Scale-ups and enterprise: Prioritize unified, real-time observability that covers performance, security, and compliance in one pane.
Investors: Winning AI companies won’t just build smarter agents. They’ll build transparent, accountable ones. The next breakout observability vendor could be worth more than the agents themselves.
Closing Thoughts
Agentic AI is powerful — but only if you can trust it. Observability turns black-box magic into measurable, manageable, and secure operations. Ignore it, and you risk costly surprises. Master it, and you unlock the true promise of autonomous systems.
Curious how others in your stage are doing it? Hit reply — I’d love to hear how you’re keeping your agents honest.