Tinqs Studio Is an Agent Harness for Game Dev

An agent harness is the layer between a raw AI model and a useful team member. It gives the agent identity, memory, tools, and guardrails. Tinqs Studio is an agent harness built specifically for game development. ## What Is an Agent Harness? A raw AI model — Claude, GPT, Gemini — is powerful but stateless. It doesn't know who you are, what project you're working on, what tools are available, or what happened yesterday. Every session is a cold start. Every conversation begins with "let me explain the project..." An agent harness solves this. It wraps around the model and provides: - Identity — who the agent is, what it values, how it should behave - Memory — what happened in previous sessions, what was decided, what failed - Tools — what the agent can actually do beyond generating text - Context — what project this is, who's asking, what infrastructure exists - Guardrails — what the agent must never do, what requires human approval Without a harness, you have a chatbot. With one, you have a team member. ## Why Game Dev Needs Its Own Harness Generic agent harnesses exist — LangChain, CrewAI, AutoGen. They're built for web apps, data pipelines, and customer support. Game development has different problems: Assets are binary. A web developer's PR is a text diff. A game developer's PR is a 150MB GLB file. Generic harnesses don't know how to preview 3D models, manage LFS bandwidth, or review binary assets. The pipeline is visual. Game dev goes from concept art to 3D model to rigged character to in-engine asset. Each step uses different tools — image generators, 3D modellers, auto-riggers, game engines. An agent harness for game dev needs to orchestrate this entire chain. Scale is physical. A web app's complexity is in business logic. A game's complexity is in geometry — 12km worlds, 155 vegetation prototypes, 576 terrain regions, 2000 crowd instances. The agent needs to understand spatial systems, GPU memory, and frame budgets. The team is small and cross-functional. A 4-person game studio has no dedicated DevOps, no dedicated artist, no dedicated PM. The harness needs to fill all those gaps, not just one. ## How Tinqs Studio Works as a Harness Tinqs Studio is a platform built on a Gitea fork with game-specific features layered on top. But the git platform is just the foundation. The harness is everything around it. ### Identity: Soul Files Every agent session starts by loading a soul file — a markdown document that defines the agent's persistent identity. Not just "you are a helpful assistant" but specific values, knowledge scope, and behavioural rules. The soul file means the agent behaves consistently whether it's triaging bugs at 9am or generating concept art at midnight. It knows what repos exist, who the team members are, what the game is about, and what decisions have been made. Identity isn't cosmetic — it's the difference between an agent that asks "what project is this?" and one that says "I see the vegetation grid was updated yesterday, want me to check the cache eviction?" ### Memory: Markdown Files in Git Agent memory is plain markdown files in a git repository. No vector databases, no proprietary stores. The agent reads its memory on session start, updates it during work, and commits changes back. This is deliberately low-tech. Markdown in git gives you version history, branching, merge conflict resolution, and human readability for free. When memory goes wrong — and it will — you can git log to see what changed and git revert to fix it. ### Tools: The CLI A single Go binary gives every agent access to: - Identity loading — full project context in 100ms - Screenshots — capture any window from outside the process - Cloud vision — send screenshots to a vision model, get structured descriptions - Health checks — verify services, repos, and tools are working - Service status — which URLs are live, what's reachable The CLI is the agent's hands and eyes. Without it, the agent can only read and write text. With it, the agent can see the game running, photograph bugs, and verify infrastructure. ### Skills: Teachable Workflows Skills are markdown playbooks that teach agents specific procedures. Instead of hoping the model figures out how to generate concept art or create a 3D model, you write the steps once: - Image Generation — generate game art with fal.ai Flux using a 4-layer prompt pattern - Concept Art Pipeline — from design brief through 2D art to 3D model export - 3D Model Generation — Tripo Studio text-to-3D and image-to-3D - Video Generation — trailer clips with OpenAI Sora 2 Skills compound. Every playbook you write makes the agent more capable. After six months, our agents handle art generation, competitive research, video production, project management, and code review — all from markdown files. ### Git Platform: 3D Preview and LFS The Gitea fork underneath handles the game-specific git problems: - 3D asset preview — rotate GLB/FBX/STL files in the browser during code review - LFS-first workflows — auto-tracking for game file extensions, storage dashboards - OAuth2 SSO — one login for git, tools, and the game - 22 format support — GLB, FBX, OBJ, STL, 3DS, PLY, and more via O3DV ### Guardrails: Human-in-the-Loop The harness defines what agents can and cannot do: - Agents can file issues, draft announcements, generate assets, write code - Agents cannot merge code, deploy builds, push to public repos, or post to external channels without human approval - The public blog repo requires human-approved merge requests — agents can propose changes but a person must review This isn't a limitation — it's a feature. The agent handles volume; the human handles judgement. ## The Cold Start Problem The biggest problem with AI agents in production isn't capability — it's context. Every new session is blank. The agent doesn't know what happened yesterday, what's in progress, or what tools are available. Most teams solve this with long system prompts. That works until your context is 200 markdown files, 15 skills, and 3 years of project history. You can't paste all of that into a system prompt. The harness solves this with staged loading: 1. CLI identity call (100ms) — loads soul file, company context, machine info, service status 2. Memory file (instant) — loads cross-session context 3. Skills (on demand) — loaded only when the task matches a skill name 4. Repo context (on demand) — read files as needed, not all upfront The agent goes from cold to fully contextual in under a second. No "let me explain..." No re-reading the same onboarding doc. Just start working. ## What Makes This Different from LangChain LangChain, CrewAI, and similar frameworks are code-first. You define agents in Python, chain them with function calls, and deploy them as services. They're powerful for building AI products. Tinqs Studio is file-first. Agents are defined in markdown. Skills are markdown. Memory is markdown. Identity is markdown. Everything is in git, readable by humans, editable without code changes, and version-controlled. This matters for game teams because: - Non-engineers can contribute. The designer writes a skill for concept art. The PM writes a skill for sprint planning. No Python required. - Everything is auditable. git log shows who changed what, when, and why. Memory changes are commits. Skill updates are diffs. - It works with any AI tool. The same soul files and skills work in Cursor, Claude Code, or any tool that reads markdown. You're not locked into one framework. ## The Stack | Layer | What | How | |——-|——|—–| | Identity | Soul files, company context | Markdown in git, loaded via CLI | | Memory | Cross-session context | Markdown in git, updated by agents | | Skills | Teachable workflows | Markdown playbooks, loaded on demand | | Tools | CLI, screenshots, vision | Go binary, one install per machine | | Git | 3D preview, LFS, SSO | Gitea fork with game-specific features | | Creative | Image gen, 3D models, video | fal.ai, Tripo, Sora 2 via skills | | Guardrails | Human approval gates | Branch protection, MR requirements | ## Getting Started If you want to build your own agent harness for game dev: 1. Start with a soul file. Write 50 words about your project's identity, values, and scope. Put it in your repo root as SOUL.md. 2. Write one skill. Pick the workflow you repeat most — concept art generation, bug triage, build verification — and write the steps as markdown. 3. Build a CLI identity command. Even a shell script that prints "project name, repos, services" gives your agent a warm start. 4. Put everything in git. Not a database, not a SaaS tool. Git. You already have it. The rest — 3D preview, LFS management, OAuth SSO, creative pipelines — you can add as you need it. Or use Tinqs Studio, where we've already built it. — An agent harness isn't a product category yet. But it should be. The gap between "I have an AI model" and "I have an AI team member" is infrastructure — identity, memory, tools, context, guardrails. For game development, that infrastructure needs to understand binary assets, visual pipelines, and spatial systems. That's what we're building.