Your AI Agents Need Memory, Not Just Storage

AI agents in 2026 are impressive. They can write code, analyze documents, manage workflows, and hold conversations that feel genuinely intelligent.

But ask an agent what happened yesterday, and you'll hit a wall.

AI agents have no memory.

Not because they can't—but because we haven't given them the right infrastructure.

The Memory Problem

Current AI agent architectures look like this:

User Request → Agent → Tool Calls → Response
                ↓
            [Context Window]
            (Limited, ephemeral)

The agent has access to:

The current conversation
Whatever tools you've connected
Maybe a vector store for "knowledge"

What it doesn't have:

History of past interactions
Sequence of actions taken
Temporal context ("last week", "before the update", "since January")
Understanding of how things change over time

Why This Matters

Consider what humans do naturally:

"Remember last month when we tried that marketing campaign? It worked for the first week, then engagement dropped. Let's not repeat that mistake."

An AI agent can't do this unless you:

Manually retrieve the historical data
Paste it into the prompt
Hope it fits in the context window
Repeat for every relevant piece of history

That's not an "agent"—that's a very sophisticated autocomplete with extra steps.

What Agents Actually Need

For AI agents to be truly useful, they need:

1. Persistent Memory

Not just "remember this conversation," but remember everything:

Every action taken
Every decision made
Every outcome observed
Every piece of context that influenced those decisions

2. Temporal Queries

The ability to ask:

"What happened last Tuesday?"
"What changed after the deployment?"
"What's the pattern over the past month?"
"When did this behavior start?"

3. Causal Understanding

Not just "what happened" but "what led to what":

"The error started after the config change"
"Customer complaints increased following the price update"
"This pattern preceded the previous outage"

4. Context Retrieval

Automatically pulling relevant history based on the current task:

Working on customer X? Here's their event history.
Debugging service Y? Here's the timeline of recent changes.
Planning feature Z? Here's what happened with similar features.

Enter MCP: AI Meets Event Sourcing

The Model Context Protocol (MCP) lets AI agents use external tools. Combined with an event store, you get agents with perfect memory.

At all.source, we've built 27 MCP tools that give AI agents full access to temporal data:

Event Tools

append_events - Record new events
get_events - Query events by entity, type, time range
get_entity_state - Reconstruct state at any timestamp
search_events - Find events matching patterns

Projection Tools

create_projection - Define materialized views
get_projection_state - Query computed state
list_projections - See all active projections

Analysis Tools

correlate_events - Find patterns across entities
get_event_frequency - Analyze event distributions
compare_timelines - Diff entity states over time

Management Tools

manage_schema - Handle schema evolution
configure_pipeline - Set up event processing
set_retention - Control data lifecycle

What This Looks Like in Practice

Example 1: Customer Support Agent

Before (no memory):

User: "Customer is complaining about billing"
Agent: "I can see their current subscription is active.
        What specific issue are they having?"

After (with event history):

User: "Customer is complaining about billing"
Agent: [Queries event history]
       "I see the issue. This customer:
        - Was charged $99 on Feb 1st (correct)
        - Then charged $99 again on Feb 3rd (duplicate!)
        - Has submitted 3 support tickets since

        Recommending: Process refund for duplicate charge
        and credit their account for the inconvenience."

Example 2: DevOps Agent

Before:

User: "Why is the API slow?"
Agent: "Current metrics show 500ms response times.
        Would you like me to check logs?"

After:

User: "Why is the API slow?"
Agent: [Queries deployment and metrics events]
       "Timeline analysis:
        - 14:00 - Response times normal (50ms)
        - 14:15 - Deployment to api-service (commit abc123)
        - 14:16 - Response times increased to 500ms
        - 14:20 - Error rate increased 10x

        The deployment introduced the issue.
        Diff shows a new database query without an index.
        Recommend: Add index or rollback."

Example 3: Business Intelligence Agent

Before:

User: "How are sales this month?"
Agent: "Current month sales are $1.2M."

After:

User: "How are sales this month?"
Agent: [Queries sales events with temporal comparison]
       "This month: $1.2M (on track for $1.5M)

        Compared to last month:
        - Enterprise deals: +40% (3 new accounts)
        - SMB deals: -15% (longer sales cycle)

        Compared to same month last year:
        - Overall: +65%
        - Win rate: Improved from 23% to 34%

        The enterprise push is working. SMB slowdown
        coincides with the pricing change on Jan 15th."

Building Memory-Enabled Agents

If you're building AI features, here's how to give your agents memory:

Step 1: Capture Everything

Every action, decision, and outcome should be an event:

{
  "type": "AgentActionTaken",
  "entity_id": "conversation-123",
  "data": {
    "action": "send_email",
    "target": "customer-456",
    "content_summary": "Billing resolution",
    "outcome": "sent"
  },
  "timestamp": "2026-02-12T10:30:00Z"
}

Step 2: Connect via MCP

Give your agent access to event tools:

{
  "mcpServers": {
    "allsource": {
      "command": "npx",
      "args": ["@allsource/mcp-server"],
      "env": {
        "ALLSOURCE_API_KEY": "your-key"
      }
    }
  }
}

Step 3: Design for Context

Structure your agents to query history before acting:

1. Receive user request
2. Identify relevant entities
3. Query event history for those entities
4. Include temporal context in reasoning
5. Take action
6. Record action as new event

Step 4: Build Feedback Loops

Use outcomes to improve:

Record what worked and what didn't
Query for similar past situations
Learn from historical patterns

The Future of Agentic AI

We're at an inflection point.

AI agents are becoming capable enough to take autonomous actions. But capability without memory is dangerous—imagine an agent that keeps making the same mistake because it can't remember the last failure.

The agents that win will be the ones with perfect memory:

Complete history of actions and outcomes
Temporal queries for context
Pattern recognition across time
Causal understanding of what leads to what

That's not science fiction. That's event sourcing + AI.

And that's exactly what we're building at all.source.

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