---
title: "Lesson 27: Advanced Sessions & Context Engineering"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Lesson 27: Advanced Sessions & Context Engineering

## [Evals x Content Engineering](https://cookbook.openai.com/examples/agents_sdk/session_memory#evals)

Ultimately, evals is all you need for context engineering too. The key question to ask is: how do we know the model isn’t “losing context” or "confusing context"?

While this full section around memory could stand on its own in the future, here are some lightweight evaluation harness ideas to start with:

- **Baseline & Deltas:** Continue running your core eval sets and compare before/after experiments to measure memory improvements.
- **LLM-as-Judge:** Use a model with a carefully designed grader prompt to evaluate summarization quality. Focus on whether it captures the most important details in the correct format.
- **Transcript Replay:** Re-run long conversations and measure next-turn accuracy with and without context trimming. Metrics could include exact match on entities/IDs and rubric-based scoring on reasoning quality.
- **Error Regression Tracking:** Watch for common failure modes—unanswered questions, dropped constraints, or unnecessary/repeated tool calls.
- **Token Pressure Checks:** Flag cases where token limits force dropping protected context. Log before/after token counts to detect when critical details are being pruned.

## 🎯 Overview

Learn advanced session management and context engineering techniques for building **production-grade, long-running agents**. This lesson teaches you how to maintain coherence, efficiency, and intelligence across extended multi-turn interactions.

## 📚 Why This Matters

**Context is a finite resource.** As conversations grow:

- Models experience "context rot" (degraded recall, confusion)
- Costs escalate (more tokens per turn)
- Latency increases (longer processing time)
- Attention budget gets stretched thin

Production agents need sophisticated context management to operate effectively over hours, handle multiple issues, and serve thousands of users.

## 🔍 What You Already Know

From previous lessons, you've mastered:

- ✅ Agent basics, tools, handoffs (Lessons 1-20)
- ✅ Basic session memory (Lesson 21)
- ✅ Vector memory for retrieval (Lesson 22)
- ✅ Evaluation and observability (Lesson 26)

## 🚀 What's NEW in This Lesson

This lesson teaches **production-grade context engineering** patterns:

1. **Context Trimming** - Keep last N turns (deterministic, low-latency)
2. **Context Summarization** - Compress older context (long-range memory)
3. **Advanced SQLite Sessions** - Conversation branching, usage analytics
4. **PostgreSQL Sessions** - Production database storage
5. **Redis Sessions** - Distributed, high-performance scaling

## 📂 Lesson Structure

Each sub-lesson is progressive and hands-on:

| Sub-Lesson                   | Topic                  | Focus                            |
| ---------------------------- | ---------------------- | -------------------------------- |
| **01_context_trimming**      | Keep last N turns      | Deterministic context management |
| **02_context_summarization** | Compress older context | Long-range memory preservation   |
| **03_advanced_sqlite**       | SQLite with analytics  | Branching, usage tracking        |
| **04_postgres_sessions**     | PostgreSQL storage     | Production database backend      |
| **05_redis_sessions**        | Distributed sessions   | High-performance scaling         |

## 🎓 Learning Path

**Recommended Order**:

1. Start with `01_context_trimming` - Foundational pattern
2. Progress to `02_context_summarization` - Enhanced memory
3. Learn `03_advanced_sqlite` - Development/debugging
4. Explore `04_postgres_sessions` - Production database
5. Master `05_redis_sessions` - Distributed scaling

**Time Estimate**: ~4-6 hours total (1 hour per sub-lesson)

## � Key Concepts

### Context vs. Memory

- **Context**: Total tokens the model attends to in one inference
- **Memory**: Persistent storage across turns (sessions, databases)

### Context Rot

As context grows, models experience:

- Degraded information retrieval
- Increased confusion across long conversations
- Higher costs and latency
- Stretched attention budget (n² pairwise token relationships)

### The Context Engineering Problem

> "Find the smallest set of high-signal tokens that maximize desired outcomes."

This means:

- **Trimming** what's no longer relevant
- **Compressing** older context into summaries
- **Storing** session state efficiently
- **Scaling** to multiple users and instances


## 🚀 Getting Started

Navigate to the first sub-lesson:

Start with [01_context_trimming](./01_context_trimming/) to learn the simplest, most common context management pattern.

🔗 Key Resources

- [OpenAI Cookbook: Session Memory](https://cookbook.openai.com/examples/agents_sdk/session_memory) - Context patterns
- [Anthropic: Context Engineering](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents) - Best practices
- [OpenAI Agents SDK](https://github.com/openai/openai-agents-python) - Official documentation

### Supplementary

- [Rise of Context Engineering](https://blog.langchain.com/the-rise-of-context-engineering/)
- [How to Fix Your Context](https://www.dbreunig.com/2025/06/26/how-to-fix-your-context.html)

![Context Engineering](./context_eng.jpeg)
