Using Asynchronous AI Agents in Software Development

Most developers don't realize that a single running AI agent is inefficient and stuck doing a single task at a time. One agent writes the code, but you end up doing the rest of the work. → You're still the architect → You’re the project planner → You’re the security analyst Multi-agent parallel execution is the next big thing in agentic development. Anthropic just showed what's possible: 200 Claude Code instances building a C compiler. But that capability wasn't available to anyone. Now it is. Open-source. SWE-AF by AgentField.ai orchestrates 500+ Claude Code instances as a fully autonomous engineering team. • Multi-pass planning refines architecture, security, and sprints via agent chains • Dependency-aware parallel execution maximizes concurrency using shared memory • Adversarial code review: agents challenge each other's work • Self-healing auto recovery adapts and replans upon failure • Auto-decomposition of hard problems • Continuous cross-agent learning What happens in one build: → Draft and review the architecture up front before coding begins → Map issues into a dependency DAG and run them in parallel across isolated worktrees → For every issue: code, test, and review → For every failure: split, rescope, or escalate → Replan the DAG if escalations happen → Final merge, integration tests, and acceptance criteria verification Give it a goal and a repo. Hundreds of agents coordinate autonomously for hours. Ships a production-ready PR with verified acceptance criteria and tracked technical debt.

🤔 AI agent frameworks follow the same loop: think, call tools, wait for ALL tools to finish, think again.                                                                                                If one tool takes 2 seconds and another takes 30 🕜 , the model sits in silence for 30+ seconds with no interaction... nothing. The user stares at "thinking..." and wonders where the productivity gain went. 😂 I've been experimenting with breaking that loop, true async agentic tools. The model dispatches a tool, gets an immediate acknowledgement, and keeps talking. Results arrive via callback whenever they're ready. The agent stays conversational the entire time. This wasn't reliably possible even 6 months ago. Models would hallucinate results instead of waiting, or lose track of which task was which. Now, they are starting to handle it. The whole implementation is ~320 lines of Python, built on the Strands Agents SDK. Three files: a decorator, a task manager, and an agent wrapper. Your tool code doesn't change at all. It works especially well for voice interfaces (dead air kills voice UX), agent-as-tool patterns where sub-agents take minutes to run, and any workflow with high-latency tools. Blog post with the full walkthrough: https://lnkd.in/gZgbQbEC Code (MIT-0): https://lnkd.in/gctuAi3y (Feel free to leave a ⭐, thanks!!)  #AI #Agents #Python #AmazonBedrock #StrandsAgents #AsyncTools

I just open-sourced a system that 10x'd my development speed with Claude Code. It's called Claude Code Orchestrator, and it enables parallel AI development — multiple Claude sessions working simultaneously on different parts of your codebase. The Problem When you use AI coding assistants sequentially, you're constantly waiting. Build authentication... wait 10 minutes. Now build the API... wait another 10. Write tests... wait again. Or try to do multiple at the same time and deal with merge conflicts / compaction loss / context rot. The Solution What if you could spawn 5 Claude sessions at once, each working on a different piece of the puzzle, without merge conflicts? This doesn't make everything perfect, but it makes parallel dev a lot easier. That's exactly what this does: /spawn auth "implement user authentication" /spawn api "create REST API endpoints" /spawn tests "write comprehensive test suite" Each worker runs in:  • Its own terminal (iterm2) tab  • Its own git worktree (isolated directory)  • Its own feature branch Zero merge conflicts. True parallelism. The Automation Layer The real magic is the orchestrator loop. Start it and walk away:  • Workers get initialized automatically  • CI status is monitored  • Code reviews run via built-in QA agents  • PRs auto-merge when all checks pass  • Finished workers clean themselves up I've been running 10+ parallel workers on complex features while focusing on architecture decisions. Built-in Quality Gates Every PR passes through specialized agents before merge:  • QA Guardian — code quality and test coverage  • DevOps Engineer — infrastructure review  • Code Simplifier — cleans up large changes (from Boris Cherny, creator of Claude Code) Try It One command to install: curl -fsSL https://lnkd.in/gAmsFhtT | bash Requirements: macOS with iTerm2 This is based on patterns from Boris Cherny, creator of Claude Code at Anthropic. The future of software development isn't AI replacing developers — it's developers orchestrating fleets of AI workers. GitHub: https://lnkd.in/gTp6wjy7  #AI #SoftwareDevelopment #Productivity #OpenSource #Claude #Anthropic

𝗜 𝗯𝘂𝗶𝗹𝘁 𝗖𝗔𝗜𝗗 𝗳𝗿𝗼𝗺 𝘀𝗰𝗿𝗮𝘁𝗰𝗵 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 - 𝘂𝘀𝗶𝗻�� 𝗺𝘆 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗦𝘄𝗮𝗿𝗺.  CAID (Centralized Asynchronous Isolated Delegation) is a multi-agent coordination framework designed for long-horizon software engineering tasks. It uses a manager agent to analyze a repository, construct a dependency graph, and delegate work units to multiple engineer agents. Each engineer runs concurrently as an asyncio coroutine, fully isolated in its own git worktree - so no agent can interfere with another. Once an engineer commits, the manager integrates changes via git merge, handles conflicts by routing them back to the originating agent, and dynamically reassigns the next ready tasks.  I didn't write this by hand. I built a Research Swarm - an autonomous agentic pipeline that reads research papers, extracts equations and architecture, generates structured implementation plans, writes production-ready Python code, and then reviews it across three dimensions: correctness, security, and paper alignment.  Using this swarm, I went from a raw research PDF to a fully implemented, reviewed, and tested CAID framework - 12 test files covering 124 test cases, a comprehensive usage guide, CLI, Python API, and full Pydantic schema validation - all in under 30 minutes and for less than $12 USD. The entire pipeline is reproducible. Point it at a paper, define an objective, and walk away. It reads, plans, builds, and verifies. Research-to-production is no longer a bottleneck. It's a pipeline. The complete codebase can be found in the 1st comment.  #AI #MultiAgentSystems #SoftwareEngineering #ResearchToCode #LLM #Agents #OpenSource

Working with Claude Code daily and following the releases allows you to see into the near future of AI. You see emergent capabilities before they hit the mainstream. I've been working on a project with complex financial simulations. A single simulation can take anywhere from ten minutes to several hours. What I've observed is a growing capability in Claude Code for multitasking and managing complex temporal relationships between tasks. Here's the progression: First came tasks. The main agent could delegate work to another instance. Useful for context management; the worker focuses on one thing while the main agent coordinates without getting bogged down in details. It's all still happening within the conversation loop. Then came subagents. Like tasks, but done by specialized models. Then background bash processes. Claude can start a long-running process in the terminal, push it to the background, and keep working on other things. It's not synchronously stuck waiting for output. It keeps working on other stuff; but it's also aware of the background processes running, and it decides when to check back on them. The other day it started a long-running simulation and told me: "This will take a while, so I'll work on these other tasks and check back later." That was an aha moment for me (see screenshot). And in the latest release: async agents. The main thread can spawn agents to do work and then simply go to sleep. No token consumption while waiting. When the agents finish, they wake up the main agent with results. So here's where I think the shift is coming: We're used to thinking of AI as a single-threaded synchronous loop. Tokens in, tokens out. But what's being built here is an agent that manages its own context window not just in terms of size, but *through time*. I'm not sure what this ends up looking like, but it seems like a great sneak peek into 2026.

𝗧𝗟;𝗗𝗥: AWS Distinguished Engineer Joe Magerramov's team achieved 10x coding throughput using AI agents—but success required completely rethinking their testing, deployment, and coordination practices. Bolting AI onto existing workflows will create crashes, not breakthroughs. Joe M. is an AWS Distinguished Engineer who has architected some of Amazon's most critical infrastructure, including foundational work on VPCs and AWS Lambda. His latest insights on agentic coding (https://lnkd.in/euTmhggp) come from real production experience building within Amazon Bedrock. 𝗧𝗵𝗲 𝗧𝗵𝗿𝗼𝘂𝗴𝗵𝗽𝘂𝘁 𝗣𝗮𝗿𝗮𝗱𝗼𝘅 Joe's team now ships code at 10x typical high-velocity teams—measured, not estimated. About 80% of committed code is AI-generated, but every line is human-reviewed. This isn't "vibe coding." It's disciplined collaboration between engineers and AI agents. But here's the catch: At 10x velocity, the math changes completely. A bug that occurs once a year at normal speed becomes a weekly occurrence. Their team experienced this firsthand. 𝗧𝗵𝗲 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗚𝗮𝗽 Success required three fundamental shifts:  • 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗿𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 - They built high-fidelity fakes of all external dependencies, enabling full-system testing at build time. Previously too expensive; now practical with AI assistance.  • 𝗖𝗜𝗖𝗗 𝗿𝗲𝗶𝗺𝗮𝗴𝗶𝗻𝗲𝗱 - Traditional pipelines taking hours to build and days to deploy create "Yellow Flag" scenarios where dozens of commits pile up waiting. At scale, feedback loops must compress from days to minutes.  • 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗱𝗲𝗻𝘀𝗶𝘁𝘆 - At 10x throughput, you're making 10x more architectural decisions. Asynchronous coordination becomes the bottleneck. Their solution: co-location for real-time alignment. 𝗔𝗰𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗖𝗧𝗢𝘀 Don't just give your teams AI coding tools. Ask:  • Can your CI/CD handle 10x commit volume?  • Will your testing catch 10x more bugs before production?  • Can your team coordinate 10x faster? The winners won't be those who adopt AI first—they'll be those who rebuild their development infrastructure to sustain AI-driven velocity.

How AI Engineers at Airtable build agents -- Airtable just shared how they built their agentic framework, which is powering features like Omni (their AI app builder) and Field Agents. How it works under the hood: - There are three main components: 1. Context Manager -> remembers guidelines, session, and event context. 2. Tool Dispatcher -> exposes predefined actions and runs them. 3. Decision Engine -> the “brain” that chooses next steps with an LLM. - It's built as an asynchronous event-driven state machine. Events like user messages, tool calls, tool outputs, and LLM responses drive the agent loop. It handles large context with trimming, schema filtering, and summarization (think MapReduce style). Errors are retriable/self-correctable so agents can recover instead of failing silently. Instead of relying on existing frameworks, Airtable built their own to keep control over prompts, context, and observability. That decision let them iterate much faster. Really nice example of how agentic systems are evolving from “tools + prompts” into full LLM-driven state machines. ♻️ Share it with anyone who’s building or thinking about agent frameworks :) I regularly share AI Agents and RAG projects on my newsletter 𝑨𝑰 𝑬𝒏𝒈𝒊𝒏𝒆𝒆𝒓𝒊𝒏𝒈 𝑾𝒊𝒕𝒉 𝑺𝒂𝒓𝒕𝒉𝒂𝒌: https://lnkd.in/gaJTcZBR Link to Airtable’s blog post: https://lnkd.in/g7qzVNDy #AI #AIAgents #LLMs