Solutions

Production-ready agent systems with self-learning, context retrieval, and infrastructure already wired up.

You’ve picked where to deploy. Now pick what to deploy.

These aren’t templates with placeholder agents. They’re complete systems with self-learning, context retrieval, and production infrastructure already wired up. Clone the repo, configure your data sources, and you’re running.

Pick a Solution

Dash

Self-learning data agent. Connects to your databases, learns your schema, improves with every query. Grounds answers in 6 layers of context.

Scout

Company intelligence agent. Navigates Slack, Drive, and wikis to answer questions. Builds a knowledge graph as it works.

Coda

Code companion. PR reviews, issue triage, architecture questions. Lives in Slack, works against your codebase.

PAL

Personal agent that learns. Knowledge base, wiki, and structured data that compounds over time.

Gcode

Self-improving coding agent. Persistent workspace, git-based isolation, full audit trail.

What’s Included

Each solution ships with capabilities that typically take weeks to build:

Capability	What It Does
Self-learning	Agent improves from feedback. Every conversation makes the next one better.
Context retrieval	RAG, knowledge graphs, multi-source navigation. Grounded answers, not hallucinations.
Production infrastructure	PostgreSQL, pgvector, webhook endpoints. Deploy to Docker, Railway, or AWS.
Interfaces	Slack, Telegram, WhatsApp ready. Add more with a few lines of config.

Improving Your Agents

Each solution includes tools for iterating on agent quality:

Solution	Improvement Tools
Dash	Automated improvement loop (python -m evals improve)
Scout	Probe library with /loop support
Coda	Eval suite for routing and synthesis
PAL	Smoke tests and behavioral evals
Gcode	LearningMachine for persistent context

Build Your Own

Don’t see what you need? Start from a starter template and add your agents.