Agentic Coding in 2026

a.k.a. "We don't need no stinkin' AGI to 10x software development"

Why pay attention to what's happening today?

Several factors are converging that make long-running, asynchronous agentic coding practical.

With tools available today you can kick off some work, have your agents work at it for hours while you sleep and wake up to production-grade results.

No need to dive into all the tools mentioned today, but essential to understand the direction our field is heading and the core skills that are becoming essential.

Factor 1: Better Models

Sustained, multi-step work is now practical

Improved Models for Long-Horizon Work

Models have gotten better at staying on track through longer tasks without losing the thread.

Meaningful improvements in tool calling and sustained reasoning across many steps.

Factor 2: The Loop Insight

Persistence beats brilliance

The Ralph Wiggum Loop

Models don't need to get smarter to handle long-horizon tasks. You just need a while loop around them.

while(! done(state)){
  state = do_step(state);
}

How the Loop Works

  • Human on the loop
  • Agent stays in the smart zone: no context compaction, context resets each turn
  • State and progress persists via external memory
  • Chips away at tasks until all requirements are satisfied
    • orchestrating LLM-as-judge
    • more deterministic: compiles + all tests pass, etc.

Factor 3: Memory upgrades

Giving agents durable recall

Beads: Trello for Agents

A shared notebook your agents can actually read back.

  • Tasks and context persisted as JSON "beads" backed by Git
  • Dependency tracking, progress tracking, cross-session memory

Factor 4: Multi-Agent Orchestration

Coordinating multiple agents

Gas Town

  • Break complex work into subtasks
  • Coordinate between specialized agents
  • Maintain shared context externally (beads)
  • Wild West, do not recommend

OpenClaw: Agents Beyond Coding

A persistent AI assistant that takes real-world actions.

  • Long-term memory + multi-agent composition
  • Full system integration in a single runtime
  • Have your personal life leaked 🚨

Claude Code

  • Adopts new innovations rapidly
  • Built into the product today, no mcp/DIY/hackage required
    • Tasklists: a lightweight trello board for tracking work
    • Subagents and agent swarms
    • Hooks: deterministic triggers that force agents to persevere until done done.
  • No need to "get me some claude ASAP", there is no moat in the harness layer.
  • 💰

What This Means for Us

The skills that matter are shifting

Fading Skills

  • Syntax memorization
  • Boilerplate coding, yet another
    • REST endpoint
    • command handler
    • screen
    • ...
  • Refactoring shortcuts

Rising Skills

  • The old that stay relevant
    • System design, walking skeletons and good examples (tm)
    • Specifications & work breakdown
    • Validating generated code (the "sniff test")
  • The new that become relevant
    • Prompt, context and harness engineering
    • Setting up, testing and debugging AI workflows
    • Multi-agent orchestration

Architects & AI Head Chefs

We're shifting from line-by-line coders to orchestrators of multi-agent AI workflows.

* Don't rush over the hill, you _will_ drive off a cliff if you skip a step. Each stage unlocks the next. * chat: exploring options, building a spec, asking questions when stuck * mid-loop: genAI as autocomplete on steroids in your IDE: gathers context, suggests code * in-the-loop: agent mode, the model writes code, compiles, runs tests, and iterates. You steer and babysit. * on-the-loop: fully autonomous but "small" workflows (think user story/screen/endpoint/...). You set up guard rails and specify desired state, agent works until done, you verify * multi-agent: fully autonomous workflows for "larger" workloads (think building a prototype, fixing a backlog of bugs,). You set up guard rails, specify desired state and multi-agent workflows