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S Tarunhiga
Aug 02, 2026
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Building an AI Software Team That Actually Collaborates

Grounded / Real Inflated / Uruttu
80% real
20% uruttu
article Original Content
I just built a 4-agent software team.
(100% local and runs on open-source stack)
Everything runs from Telegram and is managed on a Kanban board.
- A project manager who plans the work
- A backend developer
- A frontend developer
- And a tester
The PM reads a goal, breaks it into linked tasks, and assigns each to the right agent.
The thing that makes them a team instead of four strangers is a shared kanban board. Every task is a row, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know.
The next agent reads that summary before it starts. So the frontend developer never has to guess the API shape, and the tester knows exactly what to verify.
The hardest part I faced when setting this up was building an agent that could actually act like a backend engineer.
A backend engineer has to stand up a database, wire auth, manage storage, deploy functions, and keep all of it consistent while the rest of the team builds on top.
An agent doing this from scratch failed almost every time in my run.
It burned its context window, remembering which tables existed and which endpoint it created. Due to this, the work context exhausted quickly.
I solved this by adding InsForge as the backend context engineering layer. It is an open-source, agent-native backend, and I added it to our backend developer agent as a skill.
With InsForge installed, the agent stopped improvising infrastructure and followed a reliable path to create the project, define the database, set up auth, and deploy functions.
To test the whole team, I had them build a working Google Docs clone, AI features included.
The backend agent spun up the full service on its own, like database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard.
The frontend agent read that summary and built the UI on top of it, and the tester closed the loop.
verified Validated Content

This post is mostly plausible, but many claims are about the author's own implementation and can't be independently verified. The technical concepts are generally sound.

Fact check

"I just built a 4-agent software team."

  • ✅ Plausible.

  • Multi-agent software engineering systems are common today using frameworks such as LangGraph, CrewAI, AutoGen, and custom orchestrators.

"(100% local and runs on open-source stack)"

  • ✅ Plausible.

  • Entirely local agent systems can be built with open-weight models and open-source tooling.

"Everything runs from Telegram and is managed on a Kanban board."

  • ✅ Plausible.

  • Telegram bots and Kanban boards can easily be integrated into an agent workflow.

"A project manager...backend developer...frontend developer...tester."

  • ✅ Accurate.

  • This is a common multi-agent architecture.

"The PM reads a goal, breaks it into linked tasks, and assigns each to the right agent."

  • ✅ Accurate.

  • Task decomposition and routing are standard orchestration patterns.

"Shared kanban board...agents write summaries...next agent reads them."

  • ✅ Accurate.

  • Shared task state and handoff summaries are widely used to reduce context loss between agents.

"Frontend developer never has to guess the API shape."

  • 🟡 Reasonable simplification.

  • Good summaries help, but they don't guarantee the frontend won't need clarification.

"Backend agent failed almost every time."

  • 🟡 Anecdotal.

  • This may be true for the author's experiments, but it isn't a general property of AI agents.

"It burned its context window..."

  • ✅ Accurate.

  • Long infrastructure tasks can exhaust context windows or cause agents to lose track of state, depending on the model and system design.

"I solved this by adding InsForge as the backend context engineering layer."

  • 🟡 Needs verification.

  • If this reflects the author's own implementation, it's fine. If presented as a general capability of InsForge, it should be backed by documentation.

"InsForge is an open-source, agent-native backend."

  • 🟡 Likely accurate, assuming InsForge is indeed open source and describes itself this way. This should be verified against the project's documentation.

"With InsForge installed, the agent stopped improvising infrastructure..."

  • 🟡 Anecdotal.

  • This is the author's reported experience, not an objective guarantee.

"Built a working Google Docs clone, AI features included."

  • 🟡 Plausible but unverified.

  • Without a demo or repository, readers should treat this as a personal claim.

"Backend agent spun up...database tables, auth, edge functions..."

  • 🟡 Plausible.

  • Modern backend platforms can automate much of this, but the claim isn't independently verifiable.

Overall verdict

Accuracy: 8.5/10

The workflow architecture is technically sound:

  • Multi-agent collaboration ✅

  • Shared task memory ✅

  • Context handoffs ✅

  • Backend automation challenges ✅

Most of the uncertainty comes from implementation-specific claims (InsForge performance, success rates, Google Docs clone), which readers can't independently verify.

Real vs Fluff

🟢 80% Real | 🟡 20% Fluff

Real (80%)

  • Multi-agent software teams.

  • Kanban-based coordination.

  • Task decomposition.

  • Shared summaries.

  • Context-window limitations.

  • Backend automation challenges.

Fluff / Unverified (20%)

  • "Failed almost every time."

  • InsForge solving the problem so cleanly.

  • The complete Google Docs clone demonstration.

  • Performance claims based on one implementation.

Overall, it's a technically credible post, but several statements should be understood as the author's personal implementation experience rather than universal facts.