Advanced Memory Management for Agentic AI Development

AI Memory Management Hero Image - From Chaos to Order Transform your stateless LLMs into intelligent agents with proper memory architecture

Imagine building an AI assistant that forgets your name every time you say hello. That's the reality developers face with Large Language Models today—every conversation starts from zero.

You've probably experienced this frustration firsthand: explaining the same context(user preferences) repeatedly to AI Models, watching costs skyrocket as conversations grow longer, or seeing your carefully crafted agent become confused by its own conversation history. These aren't just minor inconveniences—they're fundamental barriers to building truly intelligent AI agents.

Large Language Models (LLMs) are inherently stateless, meaning they forget everything from the moment a single API call concludes. This presents a fundamental challenge for developers striving to build personalized, stateful AI agents that can hold long, meaningful interactions. The solution lies in Context Engineering—the discipline of dynamically assembling and managing all necessary information for the LLM to reason and act.

The core dilemma is that while past data is essential for intelligence, managing it quickly becomes an overwhelming problem.

Challenge 1: Why Agentic Data Explodes (And Becomes Useless)

In agentic applications, every message, tool call, tool output, and intermediate thought is an "Event" appended to the active conversation log, or Session. This history rapidly spirals out of control, introducing four major challenges:

Context Window Limits: The conversation transcript can exceed the maximum token count the LLM can process, causing the API call to fail.
Cost and Latency: Most LLM providers charge by tokens sent and received. Larger contexts increase costs and latency, resulting in a slower response time for the user.
Noise and Context Rot: As the context grows, its quality diminishes. The LLM's ability to focus on critical information suffers from context rot as conversational filler and irrelevant details flood the prompt.
Reliability Issues: As context approaches limits, agents become unpredictable— sometimes forgetting critical instructions or failing to complete tasks as the relevant information gets pushed out of the window.

Consider a simple conversation that quickly explodes:

User: "What's the weather today?"
Agent: [Tool call: weather_api] → 500 tokens
Agent: "It's 72°F and sunny..."
User: "Should I bring an umbrella?"
Agent: [Tool call: forecast_api] → 400 tokens
Agent: "No rain expected..."
User: "What about tomorrow?"
Agent: [Tool call: extended_forecast] → 600 tokens
...
# After 20 interactions: 15,000+ tokens of history!

Challenge 2: The Master Data Management (MDM) Challenge for Agents

To create a continuous, personalized user experience, an agent must transform the transient chaos of a single Session (the "workbench") into highly organized, persistent knowledge (the "organized filing cabinet"). This process is a deep challenge akin to managing data coherence in a Master Data Management (MDM) System—it requires maintaining a single, accurate source of truth for user-specific knowledge.

Note: Master Data Management (MDM) is a well known discipline in which business and information technology collaborate to ensure the uniformity, accuracy, stewardship, semantic consistency, and accountability of the enterprise's official shared master data asset.

The long-term knowledge, called Memory, must be curated to be useful. Otherwise, a simple extraction process results in a noisy, contradictory, and unreliable log. An agent memory manager must tackle the MDM problem by performing consolidation:

Conflict Resolution: Resolving contradictions when a user's preferences change over time.
Deduplication: Merging duplicative entities or facts mentioned in multiple ways.
Information Evolution: Updating initial, simple facts as they become more nuanced.
Forgetting: Proactively pruning old, stale, or low-confidence memories to keep the knowledge base relevant and efficient.

Solution: Achieving Agentic MDM with Google ADK and Vertex AI

Effective memory management requires complementary strategies for both short-term (in-session) and long-term (cross-session) memory.

i) Short-Term MDM via ADK Content Compaction

For managing the immediate Session history and fitting it within the LLM's context window, the Google Agent Development Kit (ADK) offers compaction techniques. These methods act as short-term MDM by trimming the verbose log while preserving core context:

Token-Based Truncation: Before sending the history, the agent includes messages starting with the most recent and works backward until a token limit (e.g., 4000 tokens) is reached, cutting off the rest.
Recursive Summarization: Older messages are periodically replaced by an AI-generated summary, which is then used as a condensed history. For instance, ADK's EventsCompactionConfig can trigger this LLM-based summarization after a configured number of turns.

Example: Implementing ADK Compaction

import asyncio
from google.adk.runners import InMemoryRunner
from google.adk.agents.llm_agent import Agent
from google.adk.apps.app import App, EventsCompactionConfig
from google.adk.apps.llm_event_summarizer import LlmEventSummarizer
from google.adk.models import Gemini


root_agent = Agent(
    model="gemini-2.5-flash-lite",
    name="greeter_agent",
    description="An agent that provides a friendly greeting.",
    instruction="Be Humble, Respectable and keep your answers short and sweet.",
)

# Define the AI model to be used for summarization:
summarization_llm = Gemini(model="gemini-2.5-flash")

# Create the summarizer with the custom model:
my_summarizer = LlmEventSummarizer(llm=summarization_llm)


# Configure the App with the custom summarizer and compaction settings:
app = App(
    name="root_agent",
    root_agent=root_agent,
    events_compaction_config=EventsCompactionConfig(
        compaction_interval=3,
        overlap_size=1,
        summarizer=my_summarizer,
    ),
)

# Set a Runner using the imported application object
runner = InMemoryRunner(app=app)


async def main():
    print("\n################################ START OF RUN")
    print("Tip: Use `exit` or `quit` to exit the app.\n---")
    user_query = input("User: ")
    while user_query.strip() not in ["exit", "quit"]:
        try:
            await runner.run_debug(user_query)
        except Exception as e:
            print(f"An error occurred during agent execution: {e}")
        user_query = input("User: ")
    print("#---")
    print("################################ END OF RUN\n")


if __name__ == "__main__":
    asyncio.run(main())

ii) Long-Term MDM with Vertex AI Memory Bank

For persistent, cross-session memory management (the true long-term MDM), Google offers Vertex AI Memory Bank (also known as Agent Engine Memory Bank).

This managed service is an LLM-driven ETL pipeline designed to automatically manage the entire lifecycle of long-term memory. It ensures the agent is an expert on the user, not just on external facts.

Vertex AI Memory Bank solves the MDM problem for long-term memory through:

Extraction and Consolidation: It uses LLMs to intelligently extract meaningful facts from the conversation history (Sessions) and performs the critical consolidation step to resolve conflicts and deduplicate information.
Asynchronous Generation: Critically, memory generation and consolidation run as an asynchronous background process after the agent has responded to the user, ensuring zero latency on the "hot path" of user interaction.
Persistent Storage and Retrieval: It durably stores these memories, linking them to a specific user ID, and makes them available for intelligent, similarity-search based retrieval in future sessions.

Example: Integrating Vertex AI Memory Bank

print("🧠 Creating Memory Bank configuration for hotel concierge...\n")

basic_memory_config = MemoryBankConfig(
    # Which embedding model to use for similarity search
    similarity_search_config=SimilaritySearchConfig(
        embedding_model=f"projects/{PROJECT_ID}/locations/{LOCATION}/publishers/google/models/text-embedding-005"
    ),
    # Which LLM to use for extracting memories from conversations
    generation_config=GenerationConfig(
        model=f"projects/{PROJECT_ID}/locations/{LOCATION}/publishers/google/models/gemini-2.5-flash"
    ),
)

print("✅ Memory Bank configuration created!")
print("\n🛠️ Creating Agent Engine with Memory Bank...\n")
print("⏳ This provisions the backend infrastructure for guest memory storage...")

agent_engine = client.agent_engines.create(
    config={"context_spec": {"memory_bank_config": basic_memory_config}}
)

agent_engine_name = agent_engine.api_resource.name

print("\n✅ Agent Engine created successfully!")
print(f"   Resource Name: {agent_engine_name}")
print("💬 Creating a session for guest check-in...\n")

# Generate a unique guest identifier
guest_id = "guest_emma_" + str(uuid.uuid4())[:4]

# Create a session for this guest
session = client.agent_engines.sessions.create(
    name=agent_engine_name,
    user_id=guest_id,
    config={"display_name": f"Check-in conversation for {guest_id}"},
)

session_name = session.response.name

print("✅ Session created successfully!")

Complete code: Link

By combining ADK's short-term compaction with the robust, intelligent, and managed capabilities of Vertex AI Memory Bank, developers can confidently build agents that truly remember, adapt, and personalize their interactions without facing the inevitable data explosion challenges.

Conclusion: From Stateless to Stateful AI

Memory management isn't just a technical optimization—it's the foundation that transforms LLMs from impressive demos into production-ready AI agents. Without proper memory architecture, your agents will forever remain trapped in a cycle of forgetting and re-learning, frustrating users and burning through your budget.

The combination of Google ADK's session compaction and Vertex AI Memory Bank provides a complete solution to the agentic MDM challenge:

Immediate wins with token-based truncation and summarization
Zero-latency memory through asynchronous processing
Intelligent consolidation that resolves conflicts automatically
Scalable architecture that grows with your user base

Get Started Today

Ready to build agents that truly remember? Here's your roadmap:

Start Small: Implement ADK compaction in your existing agents to immediately reduce costs and latency
Monitor Usage: Track your token consumption and identify memory-intensive workflows
Graduate to Memory Bank: Once you need cross-session persistence, integrate Vertex AI Memory Bank
Optimize Continuously: Use the consolidation rules to fine-tune what your agents remember

Don't let your AI agents suffer from perpetual amnesia. The tools exist today to build intelligent, stateful applications that deliver the personalized experiences users expect. Your users—and your infrastructure budget—will thank you.

Ready to dive deeper? Check out the ADK documentation and start building agents that remember.

Key Takeaways

TL;DR - Transform your stateless LLMs into intelligent, stateful agents with proper memory management:

✅ The Problem: LLM conversations explode exponentially, causing context limits, high costs, and degraded performance

✅ The Solution: Implement a two-tier memory architecture: - Short-term: Use ADK's compaction (truncation + summarization) to manage session context - Long-term: Use Vertex AI Memory Bank for persistent, cross-session memory with intelligent consolidation

✅ The Benefits: - Reduce token costs by up to 80% - Eliminate context window failures - Enable truly personalized, continuous user experiences - Zero-latency memory processing with async architecture

✅ Next Step: Start with ADK compaction today—it's a single config change that delivers immediate ROI

References: