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    <title>DEV Community: Kritika Sharma</title>
    <description>The latest articles on DEV Community by Kritika Sharma (@kritika_sharma_9ffe0d728c).</description>
    <link>https://dev.to/kritika_sharma_9ffe0d728c</link>
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      <title>DEV Community: Kritika Sharma</title>
      <link>https://dev.to/kritika_sharma_9ffe0d728c</link>
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      <title>My AI Agent Has Amnesia — And It’s Ruining My Business</title>
      <dc:creator>Kritika Sharma</dc:creator>
      <pubDate>Sun, 12 Apr 2026 12:23:50 +0000</pubDate>
      <link>https://dev.to/kritika_sharma_9ffe0d728c/my-ai-agent-has-amnesia-and-its-ruining-my-business-2a65</link>
      <guid>https://dev.to/kritika_sharma_9ffe0d728c/my-ai-agent-has-amnesia-and-its-ruining-my-business-2a65</guid>
      <description>&lt;p&gt;I kept running pricing experiments.&lt;/p&gt;

&lt;p&gt;Raise price → churn increases.&lt;br&gt;
Lower price → revenue drops.&lt;br&gt;
Try again → same mistakes.&lt;/p&gt;

&lt;p&gt;The problem wasn’t strategy.&lt;br&gt;
It was memory.&lt;/p&gt;

&lt;p&gt;What We Built&lt;/p&gt;

&lt;p&gt;We built ExpTrack AI — a system that remembers every business experiment and uses that memory to guide future decisions.&lt;/p&gt;

&lt;p&gt;Not dashboards.&lt;br&gt;
Not analytics.&lt;/p&gt;

&lt;p&gt;A system that learns from past mistakes before you repeat them.&lt;/p&gt;

&lt;p&gt;At its core, the system does three things:&lt;/p&gt;

&lt;p&gt;Stores every pricing experiment as structured memory&lt;br&gt;
Runs a pre-flight check before new experiments&lt;br&gt;
Uses past outcomes to generate real-time insights&lt;br&gt;
The Core Problem: Stateless Decision-Making&lt;/p&gt;

&lt;p&gt;Most businesses run experiments like this:&lt;/p&gt;

&lt;p&gt;Try something&lt;br&gt;
Observe results&lt;br&gt;
Move on&lt;/p&gt;

&lt;p&gt;But the system forgets.&lt;/p&gt;

&lt;p&gt;There is no accumulated intelligence.&lt;/p&gt;

&lt;p&gt;[&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fshm7i7wfze0np9goan22.png" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fshm7i7wfze0np9goan22.png" alt=" " width="800" height="473"&gt;&lt;/a&gt;]&lt;/p&gt;

&lt;p&gt;Even when data exists, it’s:&lt;/p&gt;

&lt;p&gt;Scattered across tools&lt;br&gt;
Not connected to decisions&lt;br&gt;
Not used proactively&lt;/p&gt;

&lt;p&gt;So teams repeat:&lt;/p&gt;

&lt;p&gt;Failed price increases&lt;br&gt;
Ineffective discounts&lt;br&gt;
Wrong assumptions&lt;br&gt;
Introducing Hindsight-Based Memory&lt;/p&gt;

&lt;p&gt;Instead of treating each experiment independently, we treat them as linked memory objects.&lt;/p&gt;

&lt;p&gt;Each experiment stores:&lt;/p&gt;

&lt;p&gt;Price change (delta %)&lt;br&gt;
Hypothesis&lt;br&gt;
Outcome (success/failure)&lt;br&gt;
Reason (what actually happened)&lt;/p&gt;

&lt;p&gt;Before running a new experiment, the system performs a memory recall.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F0x84h3imbgzf0yx7bey2.jpeg" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F0x84h3imbgzf0yx7bey2.jpeg" alt=" " width="800" height="375"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;How the System Works&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Pre-Flight Check (Before Decision)&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;When a user inputs:&lt;/p&gt;

&lt;p&gt;Current price&lt;br&gt;
Proposed price&lt;br&gt;
Hypothesis&lt;/p&gt;

&lt;p&gt;The system calls:&lt;/p&gt;

&lt;p&gt;POST /check-experiment&lt;/p&gt;

&lt;p&gt;It retrieves similar past experiments and returns:&lt;/p&gt;

&lt;p&gt;Number of related experiments&lt;br&gt;
Pattern-based insight&lt;br&gt;
Risk signal (warning or approval)&lt;/p&gt;

&lt;p&gt;Instead of guessing, the system says:&lt;/p&gt;

&lt;p&gt;“This failed before — here’s why.”&lt;/p&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fsj4c9b6suurblk709vu1.png" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fsj4c9b6suurblk709vu1.png" alt=" " width="800" height="458"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Memory Update (After Decision)&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Once the experiment is completed:&lt;/p&gt;

&lt;p&gt;PATCH /update-result&lt;/p&gt;

&lt;p&gt;The system logs:&lt;/p&gt;

&lt;p&gt;Outcome (Success / Failure)&lt;br&gt;
Actual reason&lt;/p&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fu0glze9bc3ci8pnb4gv7.png" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fu0glze9bc3ci8pnb4gv7.png" alt=" " width="800" height="470"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This closes the feedback loop.&lt;/p&gt;

&lt;p&gt;Now the agent doesn’t just store data — it learns causality.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Pattern Extraction&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Over time, the system surfaces insights like:&lt;/p&gt;

&lt;p&gt;“Price increases &amp;gt;15% fail frequently”&lt;br&gt;
“Discounting improves conversion but hurts retention”&lt;br&gt;
“Small incremental changes outperform large jumps”&lt;/p&gt;

&lt;p&gt;This is not static analytics.&lt;/p&gt;

&lt;p&gt;This is behavioral pattern learning from memory.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fz1vvyumjqm3gtalc84al.jpeg" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fz1vvyumjqm3gtalc84al.jpeg" alt=" " width="800" height="387"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;What Makes This Different&lt;/p&gt;

&lt;p&gt;Most AI tools:&lt;/p&gt;

&lt;p&gt;Respond to prompts&lt;br&gt;
Generate outputs&lt;br&gt;
Forget everything&lt;/p&gt;

&lt;p&gt;This system:&lt;/p&gt;

&lt;p&gt;Remembers&lt;br&gt;
Compares&lt;br&gt;
Adapts&lt;/p&gt;

&lt;p&gt;The difference is statefulness.&lt;/p&gt;

&lt;p&gt;Without memory → AI is reactive&lt;br&gt;
With memory → AI becomes strategic&lt;/p&gt;

&lt;p&gt;UI as a Reflection of Memory&lt;/p&gt;

&lt;p&gt;The interface is intentionally simple but structured around memory:&lt;/p&gt;

&lt;p&gt;Experiment input panel → decision entry&lt;br&gt;
Pre-flight check → memory recall&lt;br&gt;
Experiment cards → stored history&lt;br&gt;
AI insights → learned patterns&lt;/p&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fs0j8tzb4nhkyqqlliqdp.jpeg" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fs0j8tzb4nhkyqqlliqdp.jpeg" alt=" " width="800" height="376"&gt;&lt;/a&gt;&lt;br&gt;
Even locally, experiments are persisted and reused&lt;/p&gt;

&lt;p&gt;This design ensures:&lt;/p&gt;

&lt;p&gt;No loss of context&lt;br&gt;
Continuous learning&lt;br&gt;
Immediate feedback loop&lt;br&gt;
Real Behavior Change (Before vs After)&lt;br&gt;
Before&lt;br&gt;
Decisions based on intuition&lt;br&gt;
Repeated mistakes&lt;br&gt;
No structured learning&lt;br&gt;
After&lt;br&gt;
Decisions validated against past outcomes&lt;br&gt;
Early warnings before failure&lt;br&gt;
Compounding intelligence over time&lt;br&gt;
Why Hindsight Matters&lt;/p&gt;

&lt;p&gt;Hindsight is not just history.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fhx2aeg03m49lmwhejpwm.png" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fhx2aeg03m49lmwhejpwm.png" alt=" " width="800" height="458"&gt;&lt;/a&gt;&lt;br&gt;
It’s compressed experience.&lt;/p&gt;

&lt;p&gt;A single failed experiment is noise.&lt;br&gt;
Ten similar failures become a pattern.&lt;/p&gt;

&lt;p&gt;The system transforms:&lt;/p&gt;

&lt;p&gt;“We tried this before.”&lt;/p&gt;

&lt;p&gt;into&lt;/p&gt;

&lt;p&gt;“This fails under these conditions — avoid it.”&lt;/p&gt;

&lt;p&gt;What We Learned Building This&lt;br&gt;
Memory is more valuable than raw data&lt;br&gt;
Learning happens only when outcomes are tracked&lt;br&gt;
AI without context is just autocomplete&lt;br&gt;
Business decisions need historical grounding&lt;br&gt;
Small feedback loops outperform big predictions&lt;br&gt;
Where This Goes Next&lt;/p&gt;

&lt;p&gt;This system can extend beyond pricing:&lt;/p&gt;

&lt;p&gt;Marketing campaigns&lt;br&gt;
Product feature experiments&lt;br&gt;
Sales strategies&lt;br&gt;
User onboarding flows&lt;/p&gt;

&lt;p&gt;Anywhere decisions repeat → memory compounds value.&lt;/p&gt;

&lt;p&gt;Final Thought&lt;/p&gt;

&lt;p&gt;AI doesn’t fail because it’s not smart enough.&lt;/p&gt;

&lt;p&gt;It fails because it doesn’t remember enough.&lt;/p&gt;

&lt;p&gt;Once you give it memory,&lt;br&gt;
it stops being a tool…&lt;/p&gt;

&lt;p&gt;and starts becoming a system that learns with you.&lt;br&gt;
Github: &lt;a href="https://github.com/sameeralala/Exptracker.AI" rel="noopener noreferrer"&gt;Exptracker.AI&lt;/a&gt;&lt;/p&gt;

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