AI.com DOA, Agent Safety, and Splunk Migration
Published on 23.03.2026
Is AI.com Already DOA?
TLDR: AI.com launched with a splashy Super Bowl ad to make agentic AI a household reality. Weeks after launch, its future is murky. The competitive landscape moved against it before the ad even aired.
Summary:
This is a cautionary tale about timing and market dynamics. AI.com launched with significant fanfare. A Super Bowl ad. The promise of making agentic AI accessible to the masses. But by the time the ad aired, the competitive landscape had already shifted. The window had closed.
At GTC 2026, NVIDIA may have helped answer what happened. The details are sparse, but the pattern is familiar. Move fast, announce big, get outpaced. Agentic AI is a crowded space now. Every major player has an offering. Every startup has a pitch. Differentiation is hard. Execution is harder.
The question isn't whether agentic AI will happen. It will. The question is who will own the consumer mindshare. AI.com had first-mover advantage with the Super Bowl spot. But first-mover advantage means nothing without follow-through. Without clear differentiation. Without a product that delivers on the promise.
Key takeaways:
- AI.com launched with Super Bowl ad for mass-market agentic AI
- Weeks after launch, future is murky, competitive landscape shifted
- GTC 2026 may provide answers about what went wrong
- First-mover advantage means nothing without follow-through
- Agentic AI space is crowded, differentiation is hard
Why do I care:
As someone evaluating AI tools for development workflows, this matters. AI.com was positioned as the consumer gateway to agents. If it's already struggling, what does that say about the market? Two interpretations. One: the market is too crowded, consolidation is coming. Two: the bar is higher than marketers realized. Agentic AI isn't a demo. It's a production system. It needs to work reliably. It needs to integrate with existing workflows. It needs to earn trust. For developers, the lesson is clear. Don't bet on hype. Bet on execution. The agents that win will be the ones that work, not the ones with the best ads.
People Are Handing Their Agents the Keys to Everything
TLDR: Everyone's racing to deploy autonomous agents. The people who skipped the safety layer are learning an expensive lesson. Four recent examples show what skipping guardrails costs.
Summary:
This is the post I wish more teams would read before deploying agents. The premise is simple. Everyone's racing to deploy autonomous agents. Speed wins, right? Wrong. The people who skipped the safety layer are learning an expensive lesson. The incidents started piling up pretty quickly.
Four recent examples are provided. The details aren't included in the newsletter, but the pattern is clear. Agents with access to everything can do damage everywhere. Delete production data. Expose sensitive information. Make unauthorized purchases. Send emails that shouldn't be sent. The attack surface is your entire digital footprint.
The safety layer isn't optional. It's the difference between an assistant and a liability. Rate limiting. Permission scopes. Human-in-the-loop for sensitive actions. Audit logs. Rollback capabilities. These aren't features. They're requirements. The teams that deployed without them are learning the hard way. The teams that built guardrails first are sleeping at night.
Key takeaways:
- Racing to deploy autonomous agents without safety layers
- Incidents piling up quickly for teams that skipped guardrails
- Four recent examples show expensive lessons learned
- Safety layer: rate limiting, permissions, human-in-the-loop, audit logs
- Guardrails aren't features, they're requirements
Why do I care:
As a senior architect, I've seen this movie before. New technology. Excitement. Speed. Then the incident report. I've been on both sides. The team that moved fast and broke things. The team that inherited the broken things. Agents amplify this dynamic. A buggy feature annoys users. A rogue agent can bankrupt your company. The safety layer isn't about slowing down. It's about sustainable speed. You can move fast when you have guardrails. Without them, every deployment is a gamble. I'd recommend this post to any team deploying agents. Read the four examples. Learn from others' mistakes. Build the safety layer first. Your future self will thank you.
People Are Handing Their Agents the Keys to Everything
Zero-Downtime Splunk Migration at inDrive
TLDR: inDrive migrated Splunk from bare metal to AWS SmartStore with zero downtime using hybrid architecture, S3 storage, and multi-cluster search.
Summary:
This is a war story from the trenches. Ivan Saakov, Engineering Manager at inDrive Security Operations Center, shares how they migrated Splunk to AWS SmartStore. Zero downtime. That's the key achievement. For a security operations center, downtime isn't an option. You need observability. You need logs. You need to detect incidents in real-time.
The approach used a hybrid architecture. S3 storage for cost-effective log retention. Multi-cluster search for query distribution. The basic Splunk Enterprise installation steps are omitted. The focus is on migration-specific settings. The architecture diagram and mitigation strategies are referenced but not detailed in the newsletter summary.
The migration path from bare metal to cloud storage is well-trodden. But zero-downtime migrations are rare. They require careful planning. Rolling migrations. Parallel systems. Fallback options. The team at inDrive clearly did the work. The result is a case study worth studying.
Key takeaways:
- Zero-downtime migration from bare metal to AWS SmartStore
- Hybrid architecture with S3 storage for cost-effective retention
- Multi-cluster search for query distribution
- Security operations center requires continuous observability
- Migration-specific settings matter more than basic installation
Why do I care:
As someone who's architected observability systems, this resonates. Splunk is expensive. S3 storage is cheap. The cost savings alone justify the migration. But the operational complexity is real. Multi-cluster search adds moving parts. S3 introduces latency. Network egress costs can surprise you. The zero-downtime requirement multiplies the complexity. You're not migrating a system. You're migrating a running system. For teams considering this migration, I'd recommend reading the full article. The architecture details matter. The mitigation strategies matter. The lessons learned matter. inDrive did the hard work. We can learn from it.
Zero-Downtime Splunk Migration at inDrive
Why AI Agents Must Discover New Sources
TLDR: Cached retrieval misses new and long-tail sources. AI agents need link discovery on the live web to stay accurate and up to date. A Discover API provides fresh, contextual sources.
Summary:
This is a technical deep dive into agent architecture. The problem is clear. Cached retrieval misses new sources. It misses long-tail sources. Agents that rely solely on cached data become stale. They miss context. They provide outdated answers.
The solution is live web discovery. Equip agents with tools to discover fresh, contextual sources from the web. A Discover API enables this. The agent queries the API. The API returns relevant, current sources. The agent incorporates them into its response. The result is accuracy that cached data can't match.
The article promises practical insights into how to achieve this. The model for live discovery. The integration patterns. The trade-offs between freshness and latency. These details aren't in the newsletter summary, but the core thesis is sound. Agents need live data. Cached data is a starting point, not an endpoint.
Key takeaways:
- Cached retrieval misses new and long-tail sources
- AI agents need live web discovery for accuracy
- Discover API provides fresh, contextual sources
- Stale cached data leads to outdated answers
- Live discovery trades latency for freshness
Why do I care:
As someone building AI-assisted development workflows, this is crucial. I've seen agents provide outdated documentation links. Deprecated APIs. Superseded patterns. The cause is cached data. The fix is live discovery. For development tools, accuracy matters. A wrong API suggestion wastes hours. A deprecated pattern introduces bugs. I'd recommend implementing live discovery for any agent that provides technical guidance. The complexity is worth it. Your users will trust the agent more. They'll rely on it more. They'll get more value from it. Cached data is a starting point. Live discovery is the endgame.
Why AI Agents Must Discover New Sources
From Idea to Amazon KDP: Writing Your First Book
TLDR: A realistic guide to writing, publishing, and marketing your first book on Amazon KDP based on real experience, not theory.
Summary:
This is a personal journey. The author decided at thirteen to write a book about the nature of reality and the simulation hypothesis. That was roughly 30 years ago. He was very wrong. The answer depends entirely on the topic. It's not...
The newsletter excerpt cuts off, but the premise is clear. This isn't a theoretical guide. It's based on real experience. The author has published on Amazon KDP. He's made the mistakes. He's learned the lessons. He's sharing what actually works.
Amazon KDP is accessible. Anyone can publish. But publishing is easy. Selling is hard. Marketing is harder. The guide promises to cover the full journey. From idea to published book. From published book to actual sales. The realistic framing is key. This isn't "get rich quick with self-publishing." This is "here's what it actually takes."
Key takeaways:
- Realistic guide based on actual publishing experience
- Amazon KDP makes publishing accessible to anyone
- Publishing is easy, selling is hard, marketing is harder
- 30-year journey from idea to published book
- Focus on what actually works, not theory
Why do I care:
As a technical writer, I've considered writing a book. The barrier to entry is low. KDP makes it trivial to publish. But the barrier to success is high. Most self-published books sell fewer than 100 copies. Most never earn out their time investment. A realistic guide is valuable. Not the hype. Not the "I made six figures in six weeks" nonsense. The actual work. The actual marketing. The actual sales. For developers considering writing, I'd recommend this perspective. Write because you have something to say. Publish because you want to share it. Don't expect riches. Expect satisfaction. If sales come, great. If not, you've still created something.