LFG and coding agents
Coding agents write code.
LFG runs the software factory.
Claude Code and Codex are excellent coding agents. LFG gives your organization the layer above them: planning, orchestration, context, verification, and delivery.
Claude Code / Codex
One developer, one task.
Fast, and genuinely good at what it does. Everything around it — context, sequencing, review, retries — stays with the developer.
LFG
A delivery team, a whole project.
Executing in parallel, in sandboxes
Same models. Different unit of work: a task versus a delivery.
Side by side
Different jobs, not competing tools
A coding agent is scoped to a developer and a task. LFG is scoped to a delivery organization and a portfolio of work.
| Claude Code / Codex | LFG | |
|---|---|---|
| Designed for | Individual developer, one task | Delivery team, whole organization |
| Input | A prompt, an issue, a ticket | A requirement, a project, a backlog |
| Execution | One coding agent at a time | Multiple specialized agents and workflows |
| Planning | Agent-level planning within a session | Project-level planning and decomposition |
| Context | Repository and session context | Project, architecture, and organizational context |
| Quality control | The agent, its tests, and a human | Independent review, testing, and verification |
| Failures | A developer notices and intervenes | The factory retries, reassigns, or escalates |
| Models | The vendor's own model | Claude, Codex, Gemini, open models — per task |
| Output | Code, a pull request | A verified deliverable |
| What you manage | Agent sessions | Software delivery |
| What you measure | Tasks and usage | Cost, throughput, quality, rework, delivery |
Your developers should not have to manage twenty agents
Developers increasingly spend their day starting agents, feeding them context, checking their output, retrying what failed, reviewing pull requests, and coordinating what depends on what.
That is better than writing every line by hand. But it still makes the developer the orchestration layer.
LFG moves orchestration into the factory.
Where the day actually goes
- Starting agents
- Giving them context
- Checking their output
- Retrying failed tasks
- Reviewing pull requests
- Coordinating dependencies
Every one of these is coordination work. None of it is the engineering judgment you hired them for.
The orchestration layer
From requirement to verified software
Six stages. Coding agents do their best work inside stage four; the other five are the part nobody has been running for you.
LFG reads the requirement, the repository, the architecture, and the project context that already exists around them.
It settles the implementation strategy, the dependencies, and which systems the change is going to touch.
A large requirement becomes an executable graph of smaller tasks, each with acceptance criteria a reviewer can check.
Specialized coding agents pick up tasks and work them in isolated sandboxes, concurrently wherever the graph allows.
Separate agents and deterministic tooling review the code, run the tests, and validate the behavior actually changed.
Your engineers review one complete, verified change instead of supervising every agent step that produced it.
Move your engineers from supervising AI to reviewing outcomes.
Supervision scales with the number of agents. Review scales with the number of deliverables. Only one of those gets cheaper as the models get better.
Model-neutral by design
Bring the best coding agents
LFG is not another model-locked coding agent. Use the right execution engine for each piece of work.
Claude
For the work that needs complex reasoning across an unfamiliar codebase.
Codex
For implementation against a plan that is already clear.
Gemini
Wherever it wins on the task, the context window, or the price.
Open models
When unit economics or data control decide it for you. Self-hosted, your keys, your infrastructure.
Claude
GPT
Gemini
Grok
DeepSeek
Kimi
GLM
When coding models improve, your factory improves.
LFG is not a bet against the model vendors. It is a bet that capable workers keep arriving and someone has to organize them.
Verification
The agent that writes the code should not be the only agent checking it
Coding agents are probabilistic. LFG treats their output as work that has to be independently verified, not as truth.
Every change runs the same gauntlet before a human ever looks at it. Anything that fails goes back for a fix and runs it again.
Your engineers are the last gate, not the first one.
Organizational context
Build the way your company builds
A coding agent starts every session roughly where the last one ended: with a repository and a prompt. A factory accumulates.
Every project becomes context for the next one.
For whoever signs the invoice
Manage delivery, not tokens
The question a CTO or agency owner has to answer is not how much Claude usage the team burned last month. It is what shipped, how good it was, what it cost, and where the factory is failing.
Delivery dashboard
Illustrative figures36
Projects delivered
147
Tickets completed
82%
First-review acceptance
11%
Rework rate
214 hrs
Human review hours avoided
Agent cost vs. traditional cost
Per project, side by side
These numbers are a mock-up of the executive view, not a customer result. Your own numbers come out of a pilot — that is the point of running one.
Why can't I just @Claude or @Codex in Slack?
You can.
If you have an isolated ticket and you want an agent to implement it, that may be all you need. We are not going to pretend otherwise, and LFG is not worth setting up for a one-line fix.
LFG earns its place when you are managing many requirements, repositories, agents, dependencies, quality gates, and engineers at the same time — and the coordination has quietly become the bottleneck.
Claude and Codex execute the work.
LFG manages the system that delivers it.
Do I have to give up the agents my team already likes?
No. They run inside LFG. The models stay the same; what changes is who is doing the planning, sequencing, and verification around them.
Does this take engineers out of the loop?
The opposite of what people usually mean by that. Every release still passes a human gate. Engineers stop babysitting agent sessions and start reviewing finished, verified changes.
Where does it run?
On your infrastructure, with your keys, if you want it that way. The core is open source. See self-hosting.
What does it cost to find out?
One project and two weeks. You end up with your own acceptance rate, rework rate, and cost per delivery instead of ours.
Don't compare the demos. Compare the output.
Give LFG one real project. Connect your GitHub repository and let the factory work through it. Your engineers judge the result.
Prefer to read first? How it works · Self-host · Case studies