Why Aegis Memory?

The Agent Context Security Problem

Building production AI agents reveals a harsh truth: your agent’s context layer is both your biggest attack surface and your biggest bottleneck. Other tools store memories. Aegis Memory engineers context — securely.

What Happens Without Proper Memory

Context Window Limits

LLM context windows are expensive and finite. A 128K context window costs ~$0.50 per call with GPT-4. At scale, this becomes prohibitive.Result: Developers truncate context, agents forget important details.

Session Boundaries

When a context window resets (timeout, crash, new session), all learned context is lost.Result: Multi-hour agent tasks restart from zero. Users repeat themselves endlessly.

Multi-Agent Chaos

When multiple agents work together, they have no shared memory. The planner can’t tell the executor what it learned.Result: Agents duplicate work, contradict each other, or drop tasks.

No Learning Loop

Agents make the same mistakes repeatedly because there’s no mechanism to remember what worked.Result: Error patterns repeat. Good strategies aren’t reused.

The Current Landscape

What Others Offer

Solution	Approach	Limitation
Vector DBs (Pinecone, Weaviate)	Store embeddings	No agent coordination, no structure
Mem0	Personal AI memory	Single-agent focused, no ACE patterns
Rolling Context	Keep recent N messages	Loses important old context
RAG	Retrieve documents	Documents aren’t agent memories

What’s Missing

None of these solve the agent-native requirements:

Scoped Access Control - Private vs shared vs global memories
Effectiveness Tracking - Which memories actually help?
Session Continuity - Resume work after context resets
Structured Coordination - Handoffs between agents
Self-Improvement - Agents that learn from outcomes

The Aegis Approach

ACE Patterns (Agentic Context Engineering)

Based on research from Stanford/SambaNova and Anthropic, we implement patterns that make agents actually useful:

Memory Voting

Agents vote on memory usefulness. Query only effective strategies.

client.vote(memory_id, "helpful", context="Worked!")

Session Progress

Track completed/in-progress/blocked items across sessions.

client.update_session("build-api",
  completed=["auth"], in_progress="endpoints")

Reflections

Store lessons learned from failures as global knowledge.

client.add_reflection("Always validate input types",
  error_pattern="TypeError in API calls")

Playbooks

Query proven strategies before starting tasks.

strategies = client.query_playbook("pagination",
  min_effectiveness=0.5)

Three-Tier Scoping

┌─────────────────────────────────────────┐
│              GLOBAL                      │
│  (Company-wide: style guides, patterns) │
├─────────────────────────────────────────┤
│           AGENT-SHARED                   │
│  (Team-level: project context)           │
├──────────────────┬──────────────────────┤
│  AGENT-PRIVATE   │   AGENT-PRIVATE      │
│  (Planner only)  │   (Executor only)    │
└──────────────────┴──────────────────────┘

agent-private: Only the creating agent can see it
agent-shared: Explicitly shared with specific agents
global: All agents can access (best practices, company knowledge)

Performance at Scale

Operation	Aegis	Typical Vector DB
Query 1M memories	30-80ms	100-500ms
Semantic dedup	1ms	50-200ms
Batch insert 50	300ms	2-5s

When to Use Aegis

Multi-agent systems (CrewAI, LangGraph teams)

Long-running agent tasks that span sessions

Agents that need to learn from past interactions

User-facing bots that should remember preferences

Self-hosted requirements (data sovereignty)

When NOT to Use Aegis

Simple single-turn chatbots (just use context window)
Document Q&A (use RAG instead)
You need sub-10ms latency (we’re 30-80ms)

Next Steps

Installation

Get started in 5 minutes

Core Concepts

Understand the fundamentals

Getting Started

Quickstart

Integrations

Deployment

Why Aegis Memory?

Why Aegis Memory?

The Agent Context Security Problem

What Happens Without Proper Memory

The Current Landscape

What Others Offer

What’s Missing

The Aegis Approach

ACE Patterns (Agentic Context Engineering)

Memory Voting

Session Progress

Reflections

Playbooks

Three-Tier Scoping

Performance at Scale

When to Use Aegis

When NOT to Use Aegis

Next Steps

Installation

Core Concepts

​Why Aegis Memory?

​The Agent Context Security Problem

​What Happens Without Proper Memory

​The Current Landscape

​What Others Offer

​What’s Missing

​The Aegis Approach

​ACE Patterns (Agentic Context Engineering)

Memory Voting

Session Progress

Reflections

Playbooks

​Three-Tier Scoping

​Performance at Scale

​When to Use Aegis

​When NOT to Use Aegis

​Next Steps

Installation

Core Concepts

Why Aegis Memory?

The Agent Context Security Problem

What Happens Without Proper Memory

The Current Landscape

What Others Offer

What’s Missing

The Aegis Approach

ACE Patterns (Agentic Context Engineering)

Three-Tier Scoping

Performance at Scale

When to Use Aegis

When NOT to Use Aegis

Next Steps