🤖 Agentic AI

Agent Sandboxes: From E2B to AWS Lambda MicroVMs

AI agents need stateful, isolated compute that suspends when idle and resumes on demand — something between Lambda (too ephemeral) and EC2 (too expensive at rest). E2B solved this with Firecracker ...

Agent Memory Deep Dive: How It Works, Why It Breaks, and Where Research Is Headed

TLDR: Every major AI agent made a different bet on memory — files, vectors, graphs, virtual memory, temporal decay. Each is defensible. None is complete. The deeper question isn't which framework i...

Agent Memory Deep Dive #5: What a Brain-Inspired Memory Architecture Actually Looks Like

TLDR: Human memory has three systems (episodic, semantic, procedural), five properties (selective encoding, cue-based retrieval, offline consolidation, reconsolidation on recall, forgetting as feat...

Agent Memory Deep Dive #4: The Cross-Tool Memory Gap — Why Agents Are Siloed

TLDR: Every AI tool has its own memory island. Claude Code knows your project preferences. ChatGPT knows your writing style. Cursor knows your codebase. Gemini knows your research questions. None s...

Agent Memory Deep Dive #3: The Forgetting Problem — Why Append-Only Memory Breaks

TLDR: Every major memory system is append-only. After months of use, stale facts overwhelm retrieval. The fix isn't deletion — it's graduated strength decay: memories that are never accessed gradua...

Agent Memory Deep Dive #2: The Memory Landscape — 10 Approaches, No Winner

TLDR: I evaluated 10 open-source memory projects across 7 capability dimensions. They cluster into 5 archetypes: append-only stores, agent-managed virtual memory, graph-based systems, self-organizi...

Agent Memory Deep Dive #0: The State of Agent Memory — Why It's Still Broken

TLDR: Agent memory is the most visibly broken component of the current AI stack. I spent some time researching every major approach — from Claude Code's file-based system to Mem0's vector store to ...

Agent Memory Deep Dive #1: Why Your Agent Forgets — The 4 Failure Modes

TLDR: Agent memory failures aren't bugs — they're architectural gaps. Four distinct failure modes explain every frustrating 'I already told you this' moment: context overflow (finite window, silent...

Two Real Problems in the Agent Domain

I run multiple AI sessions simultaneously across providers:

Agent Landscape #2: Orchestration — Pick Your Reasoning Loop

TLDR: Pick LangGraph (34k) for complex branching logic, CrewAI (53k stars) for fast prototyping, PydanticAI (17k) for type-safe single-agent, AgentScope (26k) for batteries-included. Hermes (190k) ...

Agent Landscape #6: Coordination & Protocols — Agents Talking to Everything

TLDR: Three protocols cover the full agent communication surface: MCP (agent↔tools, 87k servers), A2A (agent↔agent, Linux Foundation), AG-UI (agent↔user, adopted by Google/AWS/MS). Adopt all three....

Agent Landscape #3: Durability — Why Your Agent Can't Survive a Restart

TLDR: Most agent frameworks lose all progress on crash. Temporal (21k) and Inngest (5.5k) solve durable execution for generic workflows, but neither is agent-aware. The gap: agent-native event sour...

Agent Landscape #1: The 5 Things Every Production Agent Needs

TLDR: Every production agent needs 5 components: orchestration (reasoning loop), durable state (survive crashes), memory (3-tier: working + long-term + session), sandboxed execution (isolated tool ...

Agent Landscape #4: Memory — Beyond Context Window Overflow

TLDR: Agents need three memory tiers: working (in context window), long-term (vector-searchable), and session (per-conversation). Letta (23k) is the most complete — agents explicitly manage their o...

Agent Landscape #5: Sandboxing — The Missing Security Boundary

TLDR: Most frameworks run tools in-process with full permissions — fine for demos, fatal in production. Daytona (73k) offers the fastest sandbox (<90ms, self-hostable, MCP-native). E2B (12k) provid...

Agent-Native: Research & Landscape

| Criterion | Agent-Native | AI-Enhanced (not this) |

Agent-Native Whitespace: Underserved Directions

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