Forem: Aleksandr Primak

The Hardest Part of Coding Isn't Coding

Aleksandr Primak — Sun, 29 Mar 2026 21:32:28 +0000

The Hardest Part of Coding Isn't Coding

It's starting.

You know the feeling. You sit down, open your laptop, and stare at your editor. You know what you need to build. You might even know exactly how to build it. But between you and the first keystroke is a wall of friction that has nothing to do with technical ability.

You check Slack. You scan Jira. You re-read yesterday's PR comments. You try to remember what branch you were on, what was blocked, what that failing test was about. You open three tabs of documentation for context you already had yesterday but your brain dumped overnight.

Forty-five minutes later, you write your first line of code.

This isn't a discipline problem. It's a physics problem.

Activation Energy: A Chemistry Lesson for Developers

In chemistry, activation energy is the minimum energy required to start a reaction. Hydrogen and oxygen can sit next to each other indefinitely — they won't become water until you add enough energy to break their existing bonds and form new ones.

The reaction itself is energetically favorable. It wants to happen. But without that initial spark, nothing moves.

Software development works the same way. The work itself is often enjoyable once you're in it. The problem is the energy required to start the reaction each day.

For a solo developer or small team, that activation energy looks like this:

Context reconstruction — What was I working on? What's the current state of the branch? Did I push my last changes?
Status inventory — What's blocked? What's waiting for review? What broke overnight?
Priority sorting — What should I work on first? Has anything changed since yesterday?
Environment setup — Pull latest changes, run migrations, make sure tests pass, check that the dev server starts
Mental model loading — Re-read the code, remember the architecture decisions, recall why you made that weird workaround on line 247

Each step is small. Together, they form a barrier that can take 30-60 minutes to overcome — every single morning. That's 5+ hours per week spent not building, just preparing to build.

And here's the cruel part: the activation energy is highest when you need it to be lowest. Monday morning after a weekend. The first day back from vacation. After a context switch to a different project. The times when momentum matters most are exactly when the friction is worst.

The Context Switching Tax

It gets worse. Activation energy isn't just a morning problem — it compounds throughout the day.

Every time you switch from coding to reviewing a PR, from reviewing to triaging a bug report, from triaging to updating documentation — you pay the tax again. Research from the University of California, Irvine found that it takes an average of 23 minutes and 15 seconds to fully regain focus after an interruption.

For a developer who also handles project management, code review, testing, and deployment — that's not an interruption. That's the entire job. You're not a developer who gets interrupted. You're an interrupt handler who occasionally writes code.

The traditional response is "just focus better" or "block your calendar" or "turn off Slack notifications." These are band-aids on a structural problem. The friction isn't coming from distractions — it's coming from the work itself being fragmented across too many roles.

What If the Work Was Already Started When You Sat Down?

Here's the thought experiment that led us to build Operum.

What if, instead of spending your first 45 minutes reconstructing context and sorting priorities, you opened your laptop and saw this:

Overnight Activity:
├── PM Agent triaged 3 new issues, prioritized by impact
├── Architect Agent reviewed #412, posted design guidance
├── Engineer Agent implemented #408, opened PR #415
├── Tester Agent validated PR #413 — all checks passed
└── 2 PRs ready for your review

The Operum dashboard — open your laptop to agents already working.

Your activation energy just dropped to near zero. You don't need to remember what you were working on — the agents continued it. You don't need to sort priorities — they're already triaged. You don't need to check what's blocked — the status is right there.

Your first action isn't "figure out what to do." It's "review what's already done."

That's a fundamentally different psychological starting point.

The Psychology of Momentum

There's a well-documented phenomenon in behavioral psychology called the Zeigarnik Effect: people remember incomplete tasks better than completed ones. Unfinished work creates a low-level cognitive tension that nags at you until the task is resolved.

Most productivity systems try to exploit this — "just start the task and your brain won't let you stop." But that advice ignores the activation energy problem. You can't leverage the Zeigarnik Effect if you can't get past the starting barrier.

Operum inverts this. When you open your laptop and see that agents have already made progress on your tasks — PRs created, tests passing, reviews posted — the Zeigarnik Effect works for you instead of against you. Your brain sees incomplete reviews, passing tests awaiting approval, progress that's almost shippable. The tension pulls you into the work.

You don't have to push yourself to start. The work pulls you in.

This is the same psychology behind streaks in fitness apps, progress bars in onboarding flows, and "daily login rewards" in games. Visible progress creates momentum. The difference is that with Operum, the progress is real — actual code written, actual tests passing, actual issues triaged.

A Day in the Life: Traditional vs. Operum

Traditional Solo Dev Workflow

8:00 AM  Open laptop
8:05     Check email, Slack, GitHub notifications
8:15     Open Jira/Linear, scan the board
8:25     Remember that failing test from yesterday
8:30     git pull, npm install, wait for dependencies
8:35     Run tests — one failing, investigate
8:45     Oh right, that was the thing I was debugging
8:50     Re-read the code, load context into brain
9:00     Finally start writing code
         ─── 60 minutes of activation energy ───
...
10:30    Teammate asks for PR review
10:35    Switch context, read their changes
10:55    Leave review comments
11:00    Try to remember what I was building
11:15    Back to coding
         ─── 45 minutes lost to context switch ───
...
2:00 PM  Need to deploy the hotfix from this morning
2:10     Write deployment script, test it
2:30     Deploy, monitor logs
2:45     Back to feature work... what was I building?
3:00     Re-read code again, finally resume
         ─── 60 minutes lost ───

Productive coding time: ~4 hours out of 8. The rest is overhead.

Operum Workflow

8:00 AM  Open Operum dashboard
8:01     See overnight activity:
         - 3 issues triaged and prioritized
         - 1 PR opened by Engineer agent (#415)
         - 1 PR validated by Tester agent (#413)
         - Architect posted design notes on #412
8:05     Review PR #413 — tests passing, clean diff
8:15     Approve and merge
8:16     Review PR #415 — spot one naming issue
8:20     Comment on PR, agents will address it
8:22     Open #412, read Architect's design guidance
8:25     Respond with one clarification
8:27     Start working on the creative problem you
         actually care about
         ─── 27 minutes from laptop open to deep work ───
...
10:30    Tester agent flags a regression in PR #416
10:31    Read the test results — clear failure report
10:35    Either fix it yourself or comment and let
         Engineer agent handle it
10:36    Back to what you were doing
         ─── 6 minutes, not 45 ───
...
2:00 PM  Close laptop. Agents continue triaging,
         testing, preparing tomorrow's review queue.

Productive creative time: ~6.5 hours out of 8. The agents handle the scaffolding.

It's Not About Replacing You

I want to be precise about what's happening here, because the framing matters.

Operum doesn't replace your judgment. It doesn't make architectural decisions for you. It doesn't merge code without your approval. What it does is eliminate the startup cost so you can spend your energy on the work that actually requires a human brain — creative problem solving, product decisions, and the code that matters.

Think of it like this: a chef doesn't become less of a chef because someone else prepped the ingredients, organized the station, and heated the pans. The chef's skill is in the cooking. Everything before that is necessary but not where the value comes from.

The agents are your prep cooks. They chop the onions. You create the dish.

The Compound Effect

The activation energy savings compound in ways that aren't obvious at first.

Weekly: 5+ hours of recovered productive time. That's an extra workday every two weeks.

Monthly: Fewer "I'll get to it Monday" deferrals. Issues don't pile up because agents keep the pipeline moving even when you step away.

Quarterly: Projects that would have stalled during busy weeks keep progressing. The agents don't have bad weeks, don't get sick, don't lose context over a long weekend.

Psychologically: The dread of "I have so much to catch up on" after time off disappears. You come back to a tidy pipeline, not a mess. This changes your relationship with the work — taking a break doesn't mean paying a tax when you return.

Try It

Operum runs six specialized AI agents — PM, Architect, Engineer, Tester, Marketing, and Community — that coordinate through GitHub to manage your development workflow. It's a desktop app that runs locally, your code stays in your environment, and it's free during public beta.

If you've ever lost an hour of your morning to context reconstruction, or felt the weight of switching between coding, reviewing, testing, and deploying — this is what we built it for.

The hardest part of coding isn't coding. It's everything around it.

Let the agents handle the everything. You handle the coding.

→ operum.ai

Ship like a team of six. Start your day in flow state, not in Jira.

Cursor Is a Tool. Operum Is a Team. — Introducing Pluggable AI Engines for Multi-Agent Orchestration

Aleksandr Primak — Mon, 23 Mar 2026 23:02:40 +0000

Presented live at the spArk Labs Cursor meetup — here's what we showed.

One AI Writes Code. Six AIs Ship Products.

Cursor is incredible. It turned every developer into a 10x engineer with AI-powered code completion, inline edits, and chat-driven development. But here's the thing — shipping software isn't just about writing code.

Between the moment someone files an issue and the moment that code reaches production, there are a dozen handoffs: triage, architecture review, implementation, testing, code review, documentation, deployment. Cursor handles one of those brilliantly. What handles the rest?

That's the problem we built Operum to solve.

What Is Operum?

Operum is an AI-powered orchestration platform that coordinates 6 specialized agents to manage your entire software development lifecycle:

Agent	Role	What It Does
PM	Project Manager	Triages issues, prioritizes work, coordinates the team
Architect	Software Architect	Reviews designs, provides architectural guidance
Engineer	Software Engineer	Writes code, creates PRs, implements features
Tester	QA Engineer	Tests PRs, validates changes, catches regressions
Marketing	Growth Lead	Creates content, manages social presence
Community	Support Manager	Monitors Discord, handles user questions

These agents don't just run in parallel — they coordinate. The PM assigns work to the Architect, who provides guidance to the Engineer, whose PR gets validated by the Tester. Every handoff, every status update, every label change happens through GitHub — transparently, auditably, automatically.

The Big Announcement: Cursor as an AI Engine

Today at the spArk Labs Cursor meetup, we announced something we're excited about: Cursor is now a pluggable AI Engine in Operum.

What does that mean?

Operum agents are backend-agnostic. Under the hood, each agent runs on an AI Engine — the runtime that powers its reasoning, code generation, and decision-making. Until now, agents ran on Claude Code. Now, you can plug in Cursor as an alternative engine.

This creates a powerful new capability: mix and match AI engines per agent based on what each does best.

┌─────────────────────────────────────────────┐
│                  OPERUM                      │
│                                              │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  │
│  │    PM    │  │ Architect│  │ Engineer │  │
│  │Claude Code│  │Claude Code│  │  Cursor  │  │
│  └──────────┘  └──────────┘  └──────────┘  │
│                                              │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  │
│  │  Tester  │  │Marketing │  │Community │  │
│  │  Cursor  │  │Claude Code│  │Claude Code│  │
│  └──────────┘  └──────────┘  └──────────┘  │
│                                              │
└─────────────────────────────────────────────┘

Why this matters

Different AI models have different strengths. Some are better at reasoning about architecture. Some are faster at generating code. Some excel at test generation. With pluggable AI Engines, you get the best model for each job, not one model forced to do everything.

Think of it like a real engineering team: you wouldn't hire the same person for PM, architecture, implementation, AND testing. You'd hire specialists. Operum does the same — but now each specialist can also run on the AI engine best suited to their role.

Cursor + Operum: Better Together

Let's be clear: Cursor and Operum are not competitors. They operate at different layers of the stack.

	Cursor	Operum
What it is	AI-powered code editor	Multi-agent orchestration platform
Scope	Code writing and editing	Full SDLC from issue to production
Agents	1 (inline AI assistant)	6 specialized agents
Workflow	Human-driven, AI-assisted	Autonomous, human-supervised
Output	Better code, faster	Shipped features, tested and reviewed

Cursor makes individual developers more productive. Operum makes the entire development process autonomous. When you use Cursor as an engine inside Operum, you get Cursor's code generation strengths amplified across a coordinated multi-agent pipeline.

Cursor is a tool. Operum is a team.

What a Real Workflow Looks Like

Here's what happens when you file an issue on a repo managed by Operum with Cursor as the Engineer's AI Engine:

PM Agent (Claude Code) picks up the issue, triages it, assigns priority, adds needs-architecture label
Architect Agent (Claude Code) reviews the issue, reads the codebase, posts architectural guidance as a comment, labels ready-for-dev
Engineer Agent (Cursor) creates a feature branch, reads the Architect's guidance, implements the change, writes tests, opens a PR
Tester Agent (Cursor) checks out the PR branch, runs the test suite, performs validation, posts results
PM Agent moves the issue to needs-review — you review a tested, documented PR

Each agent uses the AI engine best suited to its role. The PM needs reasoning and coordination (Claude Code). The Engineer needs fast, accurate code generation (Cursor). The Tester needs thorough code analysis (Cursor). All coordinating seamlessly through GitHub.

For Developers Who Want a Team, Not Just a Tool

Operum is for:

Solo founders who want to ship like a team of six
Small teams drowning in coordination overhead
Side project builders who want every hour to count
Anyone who's tired of context-switching between coding, testing, reviewing, and deploying

It runs as a desktop app (built with Tauri), locally on your machine. Your code stays in your environment. No cloud dependency.

Try It

Operum is free during public beta. Download the desktop app, connect your GitHub repo, choose your AI engines, and let six agents handle your pipeline:

Website: operum.ai
GitHub: alprimak/operum
Discord: Join the community

One AI writes code. Six AIs ship products.

Presented at spArk Labs Cursor meetup — March 2026

From Issue to Production: What Autonomous Software Development Actually Looks Like

Aleksandr Primak — Fri, 20 Mar 2026 06:04:06 +0000

From Issue to Production: What Autonomous Software Development Actually Looks Like

Less theory, more reality. Here is what happens when AI agents handle your dev pipeline.

The Promise vs. The Reality

Everyone is talking about AI agents. Most demos show a single agent writing a function, maybe running a test. Impressive for a demo. Useless for real work.

Real software development is not about writing one function. It is about the entire lifecycle: someone files an issue, someone triages it, someone designs the solution, someone builds it, someone tests it, someone reviews it, and someone ships it. That is six handoffs minimum, each with its own context, tools, and judgment calls.

What if AI agents could handle the full pipeline — not just the coding part?

That is what we built with Operum. Not a coding assistant. A team of six specialized agents that coordinate through GitHub to move issues from backlog to production.

Here is what that actually looks like in practice.

A Real Issue, Start to Finish

Let's walk through a concrete example. You create a GitHub issue:

Add rate limiting to the /api/projects endpoint
Users are hitting this endpoint too frequently. Add rate limiting with sensible defaults.

Here is what happens next — without you doing anything else.

Stage 1: Triage (PM Agent)

The PM agent picks up the new issue within minutes. It reads the description, checks priority against current sprint goals, and makes a judgment call:

Assigns priority label: priority:medium
Adds pipeline label: needs-architecture
Leaves a comment explaining the prioritization reasoning

You see this as a normal GitHub comment. No magic, no hidden state. Just a comment on your issue explaining why it was prioritized the way it was.

Stage 2: Architecture Review (Architect Agent)

The Architect agent sees the needs-architecture label and picks up the issue. It:

Reads the existing codebase to understand the current API structure
Checks what middleware patterns are already in use
Recommends an approach as a GitHub comment:

Recommended approach:
- Use express-rate-limit middleware (already in package.json)
- Apply per-route config on /api/projects
- Default: 100 requests per 15-minute window per IP
- Store in memory for now, Redis adapter later if needed
- Add X-RateLimit-* headers for client visibility

Label changes: needs-architecture → ready-for-dev

Stage 3: Implementation (Engineer Agent)

The Engineer agent picks up the ready-for-dev label. It:

Creates a feature branch: feat/issue-245-rate-limiting
Reads the Architect's guidance from the issue comments
Implements the change following existing code patterns
Writes tests
Opens a pull request linking back to the issue

The PR includes a clear description of what changed and why, referencing both the original issue and the architectural guidance.

Label changes: ready-for-dev → in-progress → needs-testing

Stage 4: Testing (Tester Agent)

The Tester agent checks out the PR branch and runs a thorough validation:

Existing test suite passes
New rate limit tests pass
Manual verification that the endpoint returns 429 after threshold
Checks that rate limit headers are present in responses
Verifies no regressions in related endpoints

Results posted as a PR comment with pass/fail details.

Label changes: needs-testing → needs-review

Stage 5: Your Turn

The issue lands in needs-review with a tested, documented PR ready for your eyes. You review the diff, check the test results, and hit merge.

Total time from issue creation to merge-ready PR: minutes, not days.

What Makes This Different From "Vibe Coding"

Let me be direct about what this is not.

This is not autocomplete on steroids. It is not a chat window where you describe what you want and hope for the best. The difference comes down to three things:

1. Specialization Over Generalization

Each agent has a focused role with specific context. The Architect agent knows your codebase patterns. The Tester agent knows your test framework. The Engineer agent follows your coding conventions.

A single general-purpose agent trying to do all of these things will produce mediocre results in each area. Six specialized agents produce focused, consistent output.

2. Coordination Through GitHub

Every decision, every handoff, every piece of context lives in GitHub — as labels, comments, and PRs. There is no hidden state. No proprietary dashboard you need to learn. You can see exactly what each agent did and why.

If an agent makes a bad call, you can see the reasoning in the issue comments and correct it. That is real accountability.

3. Human-in-the-Loop by Design

Agents propose. You approve. The merge button stays with you.

This is not about replacing developers. It is about eliminating the coordination overhead that eats 40-60% of a solo developer's time. You focus on the decisions that matter. Agents handle the rest.

Beyond Code: The Full Team

The pipeline agents handle the dev workflow, but Operum's team extends further:

Marketing Agent — Creates content, manages social presence, tracks what resonates with your audience. When you ship a feature, it can draft the announcement.

Community Agent — Monitors Discord, responds to user questions, identifies patterns in support requests that might indicate bugs or missing features.

These agents follow the same principles: specialized context, transparent actions, human approval for anything public-facing.

The Practical Impact

Here is what changes when you run Operum on a real project:

Before: You file an issue, context-switch to design it, context-switch to build it, context-switch to test it, context-switch to document it. Each switch costs 15-20 minutes of ramp-up time.

After: You file an issue and review the output. One context switch instead of five.

Before: Weekend ideas sit in your notes app for weeks because the overhead of turning them into working code is too high.

After: File the issue on Sunday night. Review a tested PR on Monday morning.

Before: Solo development means choosing between moving fast and maintaining quality.

After: Every change gets architecture review and testing, regardless of timeline pressure.

Who This Is For

Operum works best for:

Solo founders drowning in coordination overhead
Small teams (2-5) where everyone wears multiple hats
Side project builders who have limited hours and want them spent on creative work
Developer-founders who want to ship like a team of six while paying like a team of one

It runs locally on your machine — a desktop app built with Tauri. Your code never leaves your environment. No cloud dependency, no usage-based pricing anxiety.

Try It

Operum is free during public beta. Download the desktop app, connect your GitHub repo, and see what happens when six AI agents start handling your pipeline:

Website: operum.ai
Discord: Join the community
GitHub: alprimak/operum

If you have ever wished you had a team but could not afford one — this is what we built Operum for.

Ship like a team of six. Pay like a team of one.

Why Single AI Agents Aren't Enough: The Case for Multi-Agent Orchestration

Aleksandr Primak — Tue, 10 Mar 2026 15:33:42 +0000

Why Single AI Agents Aren't Enough: The Case for Multi-Agent Orchestration

The Problem with Single AI Agents

If you're a developer, you've probably used ChatGPT, GitHub Copilot, or another AI tool to help with your work. These are powerful tools, but they all share a fundamental limitation: they're generalists trying to solve specialist problems.

You ask a single AI to help with code review, then project management, then deployment strategy. Each time, it starts from scratch, without context from the previous task. Each time, it's trying to be good at everything, which means it's not particularly good at anything.

This is like hiring one employee to handle your company's accounting, marketing, and engineering. Sure, they can attempt all three, but they won't be as effective as specialists who focus on one domain.

The Limitations of Single-Purpose AI

Lack of Context Switching

When you ask a single AI model to switch between different tasks, it loses context. The knowledge it applied to your last problem doesn't automatically transfer to the next one. Each conversation is essentially starting from zero.

No Task Specialization

Complex workflows require different expertise at different stages. Code review requires different skills than deployment planning. Marketing strategy differs from community management. A single model can attempt all of these, but it lacks the specialized knowledge that domain experts possess.

Sequential Processing Only

Single-agent systems force you into linear workflows. Task A happens, then Task B, then Task C. But modern development is parallelizable—you want multiple things happening simultaneously.

Limited Integration

One AI can't automatically coordinate with all your tools. You have to manually move information between systems: from Slack to GitHub, from GitHub to your project management tool, from there to your documentation.

The Multi-Agent Solution

Multi-agent orchestration is different. Instead of one AI trying to do everything, you have multiple specialized agents, each with expertise in their domain, coordinated by an orchestrator that ensures they work together seamlessly.

Specialized Expertise

With multi-agent systems:

Your Engineering Agent understands code architecture, testing patterns, and deployment strategies
Your QA Agent knows testing best practices, edge cases, and quality metrics
Your PM Agent understands project planning, prioritization, and timeline management
Your Marketing Agent knows positioning, content strategy, and growth tactics

Each agent develops deep expertise in its domain, making better decisions than a generalist ever could.

Parallel Execution

Multiple agents can work simultaneously:

While your Engineering Agent is implementing a feature
Your PM Agent is updating the project timeline
Your Marketing Agent is preparing announcement copy
Your Community Agent is drafting Discord messages

Tasks that would take hours sequentially might take minutes in parallel.

Intelligent Coordination

The orchestrator ensures agents communicate and share context:

When Engineering finishes a feature, Testing immediately knows what needs QA
When Testing passes, Marketing knows it's ready to announce
When Community responds with feedback, Engineering knows what users need
When PM updates the roadmap, everyone's priorities align

Better Decisions

Because each agent is specialized, their decisions are better. Your Code Review Agent doesn't waste time trying to figure out marketing angles. Your Marketing Agent doesn't spend cycles on technical architecture.

Real-World Example: The Release Workflow

Let's imagine you're about to release a major feature. With a single AI:

You ask it to review the code → 20 minutes
You copy the feedback to your team → 5 minutes
You ask it to help with release notes → 15 minutes
You copy the release notes to GitHub → 5 minutes
You ask it to draft marketing copy → 20 minutes
You post to Twitter, Discord, Reddit manually → 30 minutes
You update the roadmap on your project management tool → 10 minutes

Total: 1 hour 45 minutes, sequential work

With multi-agent orchestration:

Engineering Agent reviews code, updates PR comments, suggests improvements
Tester Agent runs full test suite, reports results, flags edge cases
Marketing Agent drafts release notes, announcement copy, social media posts
PM Agent updates project roadmap, closes related issues, communicates timeline
Community Agent posts announcements to Discord, Reddit, community forums

All of this happens automatically, in parallel, with each agent leveraging its specialized knowledge.

Total: 10 minutes, coordinated across all agents

Plus, when the Community Agent sees user feedback coming in, it immediately feeds that back to Engineering, who can address concerns in real-time.

Why This Matters for Developers

Reduced Context Switching

You're not constantly switching between tools and asking the same AI to be an expert in everything. Each agent owns its domain.

Better Quality

Specialized agents make better decisions. Your code gets better review. Your testing is more thorough. Your marketing is more strategic.

Faster Execution

Parallel processing means your workflows move faster. Features ship quicker. Feedback loops are shorter.

Improved Coordination

No more manually copying information between tools. Agents share context automatically. Decisions cascade correctly.

More Time for Strategy

When agents handle the routine coordination, you have more time to focus on strategy, creativity, and decisions that only humans should make.

The Orchestration Challenge

Of course, multi-agent systems are harder to build than single agents. You need:

Specialized agent design: Each agent must be trained or configured for its specific domain
Intelligent routing: The orchestrator must know which agent to ask for which task
Context sharing: Agents must understand what other agents are doing
Conflict resolution: When agents have different opinions, how do you decide?
Accountability: You need to know which agent made which decision

This is where a good orchestration platform comes in.

Operum's Multi-Agent Approach

At Operum, we've built a multi-agent orchestration platform specifically designed for development workflows. Our six specialized agents—PM, Architect, Engineer, Tester, Marketing, and Community—each have deep expertise in their domain.

More importantly, they're orchestrated around GitHub as the coordination hub. Issues become assignments. PRs become reviews. Releases become announcements. The entire workflow flows through GitHub, which every developer already uses.

This approach gives you:

✓ Specialized expertise from agents trained for specific domains
✓ Parallel execution of coordinated tasks
✓ Intelligent routing to the right agent for each task
✓ Shared context across all agents
✓ Platform integration with tools you already use

What's Next?

Single-agent AI is still useful for specific tasks: pair programming, code generation, documentation. But for coordinating complex workflows, it falls short.

The future of AI in development isn't a single super-intelligent agent. It's specialized agents working in concert, each contributing their expertise, coordinated by an intelligent orchestrator.

If you've been frustrated with AI tools that don't quite understand your domain, that lose context between tasks, or that can't automatically coordinate with your other tools—you've been experiencing the limitations of single-agent systems.

Multi-agent orchestration is the answer.

Learn More

Operum Website: operum.ai
GitHub: alprimak/operum
Discord Community: Join our Discord community to see multi-agent orchestration in action

Operum is the AI agent orchestration platform built for developers. Six specialized agents. One coordinated workflow. GitHub as the hub. Free during public beta.

Building an AI Agent Orchestrator: How 6 Specialized Agents Coordinate Through GitHub

Aleksandr Primak — Thu, 05 Mar 2026 15:43:46 +0000

A technical deep-dive into multi-agent coordination patterns

The Problem: Solo Founders Wear Too Many Hats

If you have ever built software alone, you know the struggle. One minute you are deep in code, the next you are triaging issues, then writing docs, then responding to support requests, then trying to remember what you were coding.

Context switching is the productivity killer nobody talks about enough.

I started building Operum after a particularly brutal week where I shipped zero features despite working 60+ hours. All my time went to coordination overhead - the glue work that keeps projects moving but does not directly produce value.

The idea was simple: what if AI agents could handle the coordination, and I could focus on the creative work?

(I am writing this from DevNexus in Atlanta, where conversations about AI-assisted development are everywhere. If you are here too, I would love to connect.)

Why 6 Specialized Agents Instead of One General Agent

The first version of Operum used a single agent that tried to do everything. It was... not great.

Here is what I learned: specialized agents with focused context significantly outperform generalist agents.

Think about it like a real team:

A PM who understands the full project context and can coordinate
An architect who knows the codebase patterns and can guide decisions
An engineer who can write clean, consistent code
A QA engineer who knows what to test and how
A marketer who understands your audience
A community manager who knows your users

Each role requires different knowledge, different prompts, and different tools. Cramming all of that into one agent creates conflicts and confusion.

The Six Agents

Agent	Role	Primary Responsibility
PM	Orchestrator	Triages issues, coordinates workflow, manages pipeline
Architect	Technical Advisor	Reviews feasibility, provides architectural guidance
Engineer	Builder	Writes code, creates pull requests
Tester	Validator	Tests changes, catches bugs, approves for review
Marketing	Growth	Content creation, SEO, launch strategy
Community	Support	Discord/Twitter monitoring, user assistance

The Coordination Challenge

Multi-agent systems have a fundamental problem: how do agents hand off work without stepping on each other?

I tried several approaches:

Attempt 1: Direct Agent-to-Agent Communication

Agents talked to each other directly. Chaos ensued. Agent A would ask Agent B something while Agent B was asking Agent A something else. Deadlocks everywhere.

Attempt 2: Shared Memory

All agents read/wrote to a shared context. Better, but still problematic. Race conditions. Conflicting writes. Agents overwriting each other's work.

Attempt 3: Pipeline with Clear Handoffs (Winner)

The solution that worked: a linear pipeline where each agent has clear ownership of a stage.

backlog --> needs-architecture --> ready-for-dev --> in-progress --> needs-testing --> needs-review --> done

Each stage is a GitHub label. Only one agent owns each stage. When an agent finishes, they update the label, which signals the next agent.

No direct communication needed. GitHub is the source of truth.

GitHub as the Coordination Layer

Using GitHub for coordination was not the original plan - it emerged from necessity.

Initially, I built a custom coordination layer. It worked, but users could not see what was happening. Trust requires transparency.

GitHub solved multiple problems at once:

Visibility - Every agent action shows up as a comment or commit
Auditability - Full history of who did what and when
Familiarity - Users already know how to read issues and PRs
Integration - Works with existing workflows and tools

How It Works in Practice

You create a GitHub issue describing what you want
PM agent sees the new issue, triages it, adds the needs-architecture label
Architect agent picks up issues with that label, adds technical guidance as a comment, changes label to ready-for-dev
Engineer agent picks up, creates a branch, writes code, opens a PR, changes label to needs-testing
Tester agent checks out the PR, runs tests, adds results as a comment, changes label to needs-review
You review the PR and merge

Every step is visible. Every decision is documented.

Technical Implementation

Desktop Architecture (Tauri)

I chose Tauri over Electron for several reasons:

Rust backend - Performance and memory safety
Smaller bundle size - ~10MB vs ~150MB for Electron
Native webview - Uses system webview instead of bundling Chromium
Security - Rust's memory safety guarantees

Operum Desktop App
|
|-- Rust Backend
|   |-- Agent Process Manager (spawns/monitors agent processes)
|   |-- State Store (SQLite with WAL mode)
|   |-- IPC Handler (file-based triggers)
|   +-- GitHub Client (REST API integration)
|
+-- SvelteKit Frontend
    |-- Dashboard (real-time agent status)
    |-- Issue Pipeline View
    +-- Settings/Configuration

Inter-Process Communication

Agents run as separate processes. They communicate through file-based IPC:

Trigger files: triggers/{agent}.trigger - Contains task assignments

Response files: responses/{agent}.response - Contains task results

Why files instead of sockets or message queues?

Debuggability - You can cat a file to see what is happening
Simplicity - No connection management, no serialization libraries
Crash recovery - Files survive process crashes
Atomicity - File writes are atomic on most systems

State Management

SQLite with WAL (Write-Ahead Logging) mode provides:

Concurrent reads - Multiple agents can read simultaneously
Crash recovery - WAL survives unexpected shutdowns
Single file - Easy backup and restore
Fast writes - WAL batches writes efficiently

Agent Response Protocol

Agents report results using a structured format:

DONE: Implemented user authentication feature
ISSUE: #142
LABEL-UPDATED: in-progress --> needs-testing
PR: #143

Prefixes make parsing easy:

DONE: - Task completed successfully
REQUEST: - Needs human input or decision
ERROR: - Something went wrong

Lessons Learned

1. Agents Need Guardrails

Unconstrained agents do unpredictable things. Every agent needs:

A clear scope (what they can and cannot do)
Defined outputs (what format to respond in)
Explicit limitations (what to escalate vs handle)

2. Transparency Builds Trust

When users cannot see what agents are doing, they do not trust the output. Every action should be logged somewhere visible.

3. Human-in-the-Loop is Non-Negotiable

Agents propose. Humans approve. The final merge button stays with the human. This is not just about trust - it is about accountability.

4. Specialization Beats Generalization

Six focused agents outperform one general agent. Narrower context windows lead to more consistent behavior.

5. Local-First Simplifies Everything

Running locally means:

No security debates about where code goes
No latency from network round-trips
No usage-based pricing anxiety
Full user control

What's Next

Operum is currently in public beta. The core orchestration works, but there is more to build:

Multi-provider support - Currently Claude-powered, adding support for more LLM providers
More agent types - Security review agents, analytics agents, etc.
Custom agent creation - Let users define their own agent roles and workflows
Better state visualization - Richer pipeline UI with real-time updates
Team templates - Pre-configured agent teams for common project types

Try Operum

Operum is free during public beta. If you are a solo founder or small team drowning in coordination overhead, give it a try:

Website: operum.ai
Discord: Join the community
GitHub: alprimak/operum

Download the desktop app, connect your GitHub repo, and let six AI agents handle the coordination work while you focus on building.

I would love to hear what you think — drop by the Discord or open an issue on GitHub.

Built by a solo founder who got tired of context switching and decided to automate it away.