I built an AI agent that actually finishes tasks (closing the DONE loop)

M U — Tue, 05 May 2026 23:59:57 +0000

The Problem Nobody Talks About

Everyone is building AI agents.

LangGraph. AutoGen. CrewAI. Claude Code.

They can:

plan
reason
generate tasks

But they don’t finish.

I inspected my own system:

25 seeds (tasks)
0 completed
empty experience base

No DONE loop means:

no learning
no memory compounding
no improvement over time

The Fix: Close the Loop

I implemented a full execution cycle:

Seed → Execute → Evaluate → DONE → Store Experience

First result:

Seeds before: 25
Seeds completed: 1
Experience base: 0 → 2 entries

This was the first time the system actually learned.

The Architecture

This is not just prompting. It’s a system:

Evermind (memory)
↓
OUROBOROS (cognitive loop)
↓
Hermes (runtime)
↓
LLM (GLM-5)

Each layer has a role:

Evermind → retrieves past knowledge
OUROBOROS → enforces execution loop
Hermes → runs tasks + tools
LLM → reasoning

What Makes This Different

Most agents:

think → forget → repeat

This system:

executes → evaluates → remembers → improves

Every completed task becomes input for future tasks.

Real Example

First successful loop:

task executed
evaluation passed
7 artifacts created
experience stored

Next tasks now use that experience.

Memory That Actually Works

The system connects to:

2,508 conversations
8.9M words
indexed with full-text search

Before each task:

relevant knowledge is retrieved
injected into execution

This turns:
stateless reasoning → contextual intelligence

What’s Next

better routing using memory
automated strategy evolution
deeper knowledge graph integration

The Hard Truth

The system is not perfect:

limited API keys
simple runtime
minimal infrastructure

But it has something most systems don’t:

A closed loop.

And that changes everything.

Final Thought

AI agents don’t need more intelligence.

They need completion and memory.

That’s what makes them improve.

GitHub: https://github.com/everatlas/Riven

DEV Community: M U