Stop Prompting, Start Context Engineering

Autonomous AI agents are shifting software architecture from instruction-based workflows to goal-driven, self-directed systems. But because Large Language Models (LLMs) are inherently stateless — they forget everything outside their immediate context window.

To build agents that understand, remember, and adapt over time, enterprises now depend on Context Engineering, an emerging discipline focused on assembling and managing the right information every single agent turn.

A good analogy for this process is putting your laptop to sleep for an extended period and being able to pick up where you left off. This shift turns memory, session state, embeddings, and tool outputs into critical infrastructure workloads. Just as sleeping and waking a laptop is dependent on local drive performance, the speed of the underlying storage architecture becomes the most important performance constraint with Context Engineering.

Pure Storage addresses this challenge directly through FlashArray, FlashBlade, and Portworx, which together form the storage backbone for high-performance, stateful AI systems.

Context Engineering: The AI Agent’s Real Operating System

Context Engineering is the process of dynamically constructing the full context payload required by an AI agent, including:

• system persona and constraints
  
  
• tool definitions
  
  
• session history
  
  
• long-term memory
  
  
• retrieval-augmented documents
  
  
• external tool outputs
  
  

Agents rebuild this structure every turn, and the storage system must retrieve and persist the relevant pieces at high speed.

This is where traditional storage becomes a bottleneck, and where Pure delivers a material advantage.

Sessions & Memory: The State That Drives Autonomous AI

Every production agent has two categories of state:

Sessions:

• The entire conversation history and working state of the agent.
• Retrieved at the start of every turn.
• Written at the end of every turn.
• If slow, the agent feels slow.

Memory:

• User preferences, embeddings, RAG indexes, insights, and long-term knowledge.
• Updated asynchronously.
• Queried on-demand for reasoning.

Both depend on extremely fast and random read/write access to storage.
Both map directly to FlashArray, FlashBlade, and Portworx. FlashBlade offers exceptionally high random write performance due to the unique architecture of Pure’s Direct Flash Technology. FlashArray offers industry best latency and ease of management which Portworx can be overlaid on to provide the best and most responsive persistent storage for containers - the building blocks of today’s AI pipelines.

The Storage Constraint: Speed, Parallelism, and Durability

The LLM is not the bottleneck.
The prompt is not the bottleneck.
Storage is the bottleneck for AI at scale.

Enterprises need:

• fast session retrieval
  
  
• durable memory persistence
  
  
• low-latency embedding lookups
  
  
• high-throughput document retrieval
  
  
• scalable object and file storage
  
  
• reliable database persistence
  
  
• container-native volumes for AI microservices
  
  

This is exactly where FlashArray, FlashBlade, and Portworx excel.

Why Pure Storage Is the Ideal Foundation for Context Engineering

 

 

1. Sessions Run on the Hot Path — FlashArray Provides Predictable Low Latency

Each agent turn depends on:

• retrieving prior session state
  
  
• writing new conversation state
  
  
• persisting tool metadata
  
  
• handling small, frequent, high-IOPS transactions
  
  

FlashArray enables this with:

• consistent sub-millisecond latency
  
  
• fast transactional I/O
  
  
• predictable performance under concurrency
  
  
• no tuning, tiering, or garbage-collection surprises
  
  

Whether your session store is PostgreSQL, MongoDB, Redis, AlloyDB, or MySQL, FlashArray keeps latency predictable, which keeps agents responsive.

2. Memory Generation Is Write-Heavy and Parallel — FlashBlade Handles It Effortlessly

Memory pipelines generate embeddings, summaries, metadata, and RAG indexes.
This requires:

• high-throughput reads of source documents
  
  
• high-speed writes of embeddings and vector indexes
  
  
• parallel ingest of PDFs, logs, JSON, and knowledge artifacts
  
  
• fast retrieval for RAG queries
  
  

FlashBlade is ideal for this because it supports:

• scalable, parallel NFS and S3 workloads
  
  
• massive ingest for memory and indexing jobs
  
  
• fast object storage for embeddings and vector DBs
  
  
• linear scaling without rebalancing
  
  

FlashBlade acceleration directly improves:

• RAG recall speed
  
  
• embedding generation throughput
  
  
• memory consolidation
  
  
• vector DB indexing performance
  
  

3. Portworx Enables Container-Native AI Memory and Session Management

Portworx provides the reliable data layer for AI microservices and agent runtimes running on Kubernetes.

It adds:

• highly-available, container-native volumes
  
  
• zero-downtime updates for memory stores
  
  
• instant cloning and snapshot capabilities for RAG indexes
  
  
• fast recovery of stateful AI services
  
  
• multi-zone and multi-region failover
  
  
• automated scaling of storage resources
  
  

Portworx ensures your session store, memory store, vector DB, and document services remain resilient even under concurrency spikes or node failures.

Tools Return Large Outputs — FlashBlade Makes It Efficient

AI tools frequently generate:

• long SQL result sets
  
  
• log files
  
  
• multi-MB API responses
  
  
• PDFs, HTML, XML, and images
  
  

Best practice:

• store tool outputs externally on FlashBlade (NFS or S3)
  
  
• return only pointers or IDs to the LLM
  
  

This avoids:

• context window explosion
  
  
• high token usage
  
  
• slow inference
  
  

FlashBlade’s throughput and parallelism make this approach extremely efficient for agent workflows.

Pure Storage: The Architecture for High-Performance, High Success AI

Building trustworthy agents requires:

• fast retrieval
  
  
• durable memory
  
  
• predictable latency
  
  
• consistent behavior
  
  
• no silent data delays
  
  

Context Engineering depends on a storage layer that acts like a transactional memory system, not a passive log.

FlashArray, FlashBlade, and Portworx together provide:

• low-latency session persistence
  
  
• high-throughput memory pipelines
  
  
• scalable vector indexing
  
  
• fast multimodal document retrieval
  
  
• container-native durability and replication
  
  
• predictable performance even at scale
  
  

Your LLMs are the “brain.”

FlashArray, FlashBlade, and Portworx, working together, provide the memory and reflexes that make autonomous AI possible.