Context Engineering: The Hidden Skill Behind Reliable AI Agents

Q: Agent Memory Architectures

Episodic Memory Stores specific events and experiences. "Last time we processed this type of request, we did X and the result is Y." Use case: Learning from past interactions to improve future performance. Implementation: Event logs stored in a database, retrieved by similarity search. Semantic Memo

Q: RAG vs. Agent Memory: When to Use Which

A common question in 2026: "Should I use RAG or agent memory?" The answer is: use both, for different purposes. Use RAG when: You have a large, relatively static knowledge base You need to retrieve specific facts or documents The information doesn't change frequently You want to ground the agent's r

In 2024, everyone talked about prompt engineering. In 2025, retrieval-augmented generation (RAG) became the hot topic. In 2026, the most important skill for building reliable AI agents is context engineering.

Context engineering is the art and science of managing what information an AI agent has access to at any given moment — and how that information is structured, prioritized, and updated over time.

It’s the difference between an agent that works beautifully in a demo and one that actually handles real-world complexity.

The Context Window Problem

Modern LLMs have context windows ranging from 128K to 1M+ tokens. That sounds like a lot until you realize:

A typical enterprise knowledge base contains millions of documents
A complex agent workflow might involve hundreds of tool calls and results
A long-running agent conversation can accumulate tens of thousands of tokens
Each token in the context window competes for the model’s attention

The attention mechanism in transformers doesn’t treat all tokens equally. Information at the beginning and end of the context window tends to receive more attention than information in the middle — a phenomenon known as „attention degradation“ or the „lost in the middle“ problem.

This means that simply stuffing all available information into the context window doesn’t work. You need to be strategic about what to include, what to summarize, and what to retrieve on demand.

Context Engineering Techniques

1. Prompt Compression and Summarization

When you have more context than fits in the window, you need to compress it:

Technique: Progressive Summarization

As an agent works through a task, periodically summarize the intermediate results and replace the raw output with the summary.

„`

Raw tool output (2000 tokens) → Summary (200 tokens) → Agent continues

„`

Technique: Hierarchical Summarization

For very long documents or conversations, create multiple levels of summaries:

Level 1: Full content (for the most important items)
Level 2: Detailed summary (for moderately important items)
Level 3: One-line summary (for background context)

2. Hierarchical Memory Architecture

Production agents in 2026 typically use a four-tier memory system:

Working Memory (in-context)

What the agent is actively thinking about right now
Limited to the current context window
Highest fidelity, highest cost

Short-term Memory (session-scoped)

Information from the current session/conversation
Stored externally, retrieved as needed
Medium fidelity, medium cost

Long-term Memory (persistent)

Learned facts, user preferences, historical interactions
Vector database or structured storage
Lower fidelity, but persistent across sessions

Procedural Memory (configurable)

Rules, workflows, and standard operating procedures
Defined by the development team
Changed through updates, not through agent learning

3. Selective Context Injection

Not all information should be in the context window at all times. Selective injection means:

**Retrieval on demand:** Only pull information when the agent needs it
**Relevance scoring:** Rank potential context items by relevance to the current task
**Recency weighting:** Prioritize recent information when appropriate
**Source diversity:** Avoid over-representing a single source or perspective

4. Context Window Management

Practical strategies for managing the context window:

**Sliding window:** Keep only the most recent N tokens, summarize older content
**Importance-based retention:** Score each piece of context by importance, drop low-scoring items when the window fills
**Checkpoint and restart:** For very long tasks, save state periodically and restart with a fresh context window plus a summary of prior work

Agent Memory Architectures

Episodic Memory

Stores specific events and experiences. „Last time we processed this type of request, we did X and the result is Y.“

Use case: Learning from past interactions to improve future performance.

Implementation: Event logs stored in a database, retrieved by similarity search.

Semantic Memory

Stores general knowledge and facts. „Our company’s refund policy allows returns within 30 days.“

Use case: Answering questions that require factual knowledge.

Implementation: Vector database with embedded documents, retrieved via semantic search.

Procedural Memory

Stores how-to knowledge and workflows. „To process a refund, first verify the purchase date, then check the refund eligibility rules, then initiate the refund.“

Use case: Executing multi-step workflows reliably.

Implementation: Structured workflow definitions, often implemented as agent tools or state machines.

Working Memory

Stores the current task state. „I’m currently processing a refund request for order #12345. The purchase date is within the 30-day window. The next step is to check eligibility rules.“

Use case: Maintaining state within a single task execution.

Implementation: The agent’s context window, plus any in-memory state management.

RAG vs. Agent Memory: When to Use Which

A common question in 2026: „Should I use RAG or agent memory?“

The answer is: use both, for different purposes.

Use RAG when:

You have a large, relatively static knowledge base
You need to retrieve specific facts or documents
The information doesn’t change frequently
You want to ground the agent’s responses in verified sources

Use Agent Memory when:

You need to maintain state across a conversation or workflow
The agent needs to remember user preferences or history
You’re building a long-running agent that accumulates knowledge over time
The information is dynamic and changes based on agent actions

Use Both when:

You’re building a production agent that needs both factual grounding and state management
The agent needs to retrieve information from a knowledge base AND remember what it has already done in the current session

Practical Implementation: Building a Memory Layer

Here’s a simplified example of implementing a memory layer for an agent:

„`python

from dataclasses import dataclass, field

from typing import List, Optional

import numpy as np

@dataclass

class MemoryEntry:

content: str

timestamp: float

importance: float = 1.0

source: str = „agent“

embedding: Optional[np.ndarray] = None

class AgentMemory:

def __init__(self, vector_store, max_working_memory=4000):

self.working_memory: List[MemoryEntry] = []

self.short_term: List[MemoryEntry] = []

self.vector_store = vector_store

self.max_working_memory = max_working_memory

def add(self, content: str, importance: float = 1.0, source: str = „agent“):

entry = MemoryEntry(

content=content,

timestamp=time.time(),

importance=importance,

source=source

)

self.working_memory.append(entry)

If working memory is full, summarize and move to short-term

if self._working_memory_tokens() > self.max_working_memory:

self._compress_working_memory()

def retrieve(self, query: str, top_k: int = 5) -> List[str]:

Search both short-term and long-term memory

short_term_results = self.vector_store.search(

query, filter={"source": "short_term"}, top_k=top_k

)

long_term_results = self.vector_store.search(

query, filter={"source": "long_term"}, top_k=top_k

)

Combine and rank by relevance + recency + importance

all_results = short_term_results + long_term_results

return self._rank_results(all_results)[:top_k]

def _compress_working_memory(self):

„““Summarize working memory and move to short-term storage“““

summary = self._summarize([e.content for e in self.working_memory])

self.short_term.append(MemoryEntry(

content=summary,

timestamp=time.time(),

importance=max(e.importance for e in self.working_memory),

source=“summary“

))

self.working_memory = []

def _working_memory_tokens(self) -> int:

return sum(len(e.content.split()) for e in self.working_memory)

def _summarize(self, contents: List[str]) -> str:

In production, this would use an LLM to summarize

return “ | „.join(contents[-3:]) # Simplified

def _rank_results(self, results) -> List[str]:

Rank by relevance, recency, and importance

return sorted(results, key=lambda r: r.score * r.importance, reverse=True)

„`

Conclusion: Context Engineering Is the New Prompt Engineering

Prompt engineering was about crafting the perfect input. Context engineering is about managing the entire information ecosystem that an agent operates within.

As agents become more capable and handle more complex tasks, the quality of their context management becomes the primary differentiator between agents that work and agents that fail.

Master these techniques — hierarchical memory, selective injection, progressive summarization, and the right combination of RAG and agent memory — and you’ll be building agents that can handle real-world complexity reliably.

The future belongs to the teams that master context engineering.

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*This is the final post in our August 2026 content series. All four posts are staged and ready for publishing when WP authentication is restored.*