Why Syntropic137?

You can run an AI agent in your terminal right now. So why build a platform around it?

Because running agents is easy. Running them repeatably, observably, and safely at scale is hard. Syntropic137 closes that gap.

Repeatable Workflows

Multi-phase pipelines defined in YAML. Research, plan, implement, review: every workflow runs the same way, every time, on Claude Code or OpenAI Codex.

Immutable Event Store

Every state change is a permanent, queryable event. Domain events, observability telemetry, and conversation logs: nothing is ever lost.

GitHub-Native Triggers

Event triggers enable self-healing CI, auto-responses to review comments, and PR-driven workflows. Works zero-config, no tunnel required.

Workspace Isolation

Every agent runs in an ephemeral Docker container. setup credentials are cleared before the agent starts. Egress proxies control outbound traffic.

The Problem

When you run a coding agent in your terminal, you get results, but you lose everything else:

No record of what the agent did, which tools it called, or how much it cost
No way to replay a successful workflow on a different repo
No isolation: the agent has access to your entire machine
No triggers: you have to manually kick off every task
No second opinion: reviewing work with a different vendor's model means running it by hand

What Syntropic137 Adds

Capability	Without Syntropic137	With Syntropic137
Execution	One-off terminal sessions	Multi-phase workflows with YAML definitions
Harness	Whichever CLI you happen to have open	Claude Code or OpenAI Codex, chosen per phase in the workflow
Observability	Read the terminal output	Token usage, tool timelines, cost tracking, session replay
Isolation	Runs on your machine	Ephemeral Docker containers with secret injection
Automation	Manual start	GitHub webhooks, scheduled triggers, event-driven pipelines
Data	Gone when the terminal closes	Event-sourced: every state change is permanent and queryable
Cost	Check your API dashboard	Per-session, per-workflow, per-org cost breakdown

Who It's For

Engineering teams using AI agents for code review, research, and automation
Platform engineers who need observability and cost controls around agent usage
Solo developers who want repeatable workflows without building infrastructure

Next Steps

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