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description: A critical look at two dominant approaches to AI data infrastructure — adding specialized layers (semantic caching, CDC, memory stores) versus consolidating...
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# We'll End Up Where We Started. Except with Different Technology.

**[Event Store](https://daily.dev/sources/eventstore)** · 20 min read · 0 upvotes · 0 comments

## Summary

A critical look at two dominant approaches to AI data infrastructure — adding specialized layers (semantic caching, CDC, memory stores) versus consolidating everything into an existing backend database. Both approaches are shown to share the same fundamental gap: they capture current state but not the full history of how that state was reached. Drawing parallels to Redis architecture debates from a decade ago, the author argues that state-first architectures lack auditability, causal ordering, and deterministic recovery. The proposed alternative is an event-native approach where immutable, ordered event streams serve as the primary data primitive, enabling replay instead of re-inference, natural auditability, and vendor independence. KurrentDB is positioned as the missing event-history layer that complements rather than replaces existing AI stack components.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.kurrent.io/blog/we-will-end-up-where-we-started>

## Questions this post answers

### What is the difference between using CDC (change data capture) and an event log for AI agent context?

CDC only exposes the latest state of data after a change, not the sequence of events, intermediate steps, or causal chain that produced it. An event log captures the ordered, immutable history of what actually happened, so an AI system can reason over the full sequence rather than just a snapshot of current truth, and recovery can be deterministic replay instead of re-inference.

_Teams weighing CDC pipelines against event logs for agent memory can track this architecture debate on daily.dev._

### Why isn't agent checkpointing enough for reliable recovery in multi-step AI workflows?

A checkpoint only records where an agent stopped, such as failing at step 4 of 7, not what it did in the prior steps, what data it retrieved, or what decisions it made. Recovering from a checkpoint typically means re-running the logic and re-invoking the model, hoping to land in the same result, which is not guaranteed to be deterministic and can duplicate reasoning costs.

_Developers designing agent recovery strategies can follow this checkpointing versus event-replay discussion on daily.dev._

## Similar posts on daily.dev

- [Time Travel for Data: Traditional Workarounds vs. the Kurrent Way](https://daily.dev/posts/time-travel-for-data-traditional-workarounds-vs-the-kurrent-way-bt7hnlpdm) · Event Store · 0 upvotes · 0 comments
- [Writing Events Is All You Need](https://daily.dev/posts/writing-events-is-all-you-need-rjvxyoggv) · Event Store · 0 upvotes · 0 comments
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---

Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#architecture](https://daily.dev/tags/architecture), [#redis](https://daily.dev/tags/redis), [#ai-infrastructure](https://daily.dev/tags/ai-infrastructure), [#change-data-capture](https://daily.dev/tags/change-data-capture)

[View this post on daily.dev](https://daily.dev/posts/we-ll-end-up-where-we-started-except-with-different-technology--5h7gm44uu)

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