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 - 𝘂𝘀𝗶𝗻𝗴 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

AI coding assistants are evolving from IDE copilots to autonomous teammates. LangChain just accelerated this shift by open-sourcing Open SWE, and I got the chance to try it out last weekend. Open SWE is a step towards open coding agents, but it's not a silver bullet. Here are my honest takeaways: (1) The multi-agent architecture is the standout feature. Having a dedicated Planner research the code before writing and a Reviewer check the work before opening a PR leads to far more reliable results than single-pass generation. Similar to Claude Code’s subagents, but built-in. (2) The human-in-the-loop controls are excellent. The ability to pause, edit the plan, and provide new instructions mid-task solves a major frustration I've had with other coding agents. (3) It’s overkill for simple tasks. The robust planning and review process that makes it great for complex features is inefficient for one-line bug fixes. You have to pick the right tool for the job. (4) Performance is tightly coupled to Anthropic models. The prompts are specifically tuned for Claude, and you'll likely see a drop in quality with other providers (I tried GPT-5). For most day-to-day coding, it's not ready for prime time. The overhead for simple fixes is too high, and its dependencies are a significant consideration. Also, we're still figuring out how to best use asynchronous coding agents such as Cursor’s Background Agent and Devin. This means devs will need more opinionated handholding to fully leverage Open SWE. That said, I expect the open-source community to iterate quickly. Open SWE might find its footing first in hobby projects and dev tooling sidequests — where slower iteration is tolerable and agent autonomy can be genuinely useful. Repo link and my recent post covering async coding agents in the comments below.

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.

Google's latest Gemini API update is one of the more important infrastructure releases for developers building AI agents. Until now, long-running autonomous workflows required developers to keep HTTP connections alive while agents executed tasks that could last minutes—or even hours. If the connection dropped, the workflow often failed or required significant orchestration logic. The new server-side asynchronous execution changes that model entirely. Developers can now submit an agent request with background execution enabled, immediately receive an operation ID, and allow Google's infrastructure to manage execution while applications remain responsive. Combined with native Remote Model Context Protocol (MCP) support, Gemini can securely connect directly to external services without custom middleware, while managing authentication, credential refresh, and tool routing. Why this matters: • Less custom orchestration infrastructure • More reliable long-running AI workflows • Better scalability for enterprise deployments • Lower operational complexity • Faster development cycles As foundation models become increasingly capable, competitive advantage is shifting toward inference infrastructure, orchestration, reliability, and operational resilience. The organizations that build dependable AI systems—not just intelligent ones—will be in the strongest position to deploy AI at scale. #AI #Google #Gemini #ArtificialIntelligence #EnterpriseAI #MachineLearning #AIAgents #SoftwareEngineering #Automation #Innovation

The Traditional SDLC is Broken. It’s Time for the Agentic Era (ADLC). 🚀 Let’s be honest: the traditional Software Development Lifecycle (SDLC) shown on the left is full of friction. It’s linear, slow, and heavily dependent on manual human toil—from endless backlog refinement meetings to copy-pasting context between tickets and code. We need a shift. Enter the Agentic Development Lifecycle (ADLC). As visualized on the right, ADLC isn't about replacing developers. It's about wrapping specialized AI agents around every stage of development to handle the repetitive, cognitive load. This transforms a static process into a dynamic, automated, and deeply integrated workflow where humans focus on high-value decisions. The core philosophy of ADLC: 🤖 Jira-centric orchestration: Agents live where the work lives. 🔒 Secure runtimes: "Engineer agents" don't just write code; they test it in safe sandboxes. 🗣️ Human-in-the-loop: Agents draft, suggest, and verify. Humans approve. Where to Start: The "Low-Risk" Starter Set Don't try to replace your entire pipeline overnight. The path to an ADLC starts by augmenting existing workflows without risking production. Here are 4 simple steps to start your campaign, grounded in the themes above: Step 1: Clean up the Intake (Stop bad tickets fast) Deploy a Clarifying Triage Agent. Instead of engineers chasing down missing requirements, let an agent detect vague Jira tickets and ask structured clarifying questions immediately. Goal: Zero "made-up" ticket details. Step 2: Accelerate Planning (Endless refinement meetings) Use an Epic/Story Breakdown Agent. Turn a "one-pager" feature request into a realistic draft plan—breaking it into backend, frontend, and QA tasks with proposed acceptance criteria before the team even sees it. Step 3: Safely automate the build loop Introduce an Engineer Agent with a secure runtime. Don't just ask AI to "write code once." Give it a sandbox to run an implementation/test/fix loop on smaller tasks until tests pass, then open a PR for human review. Step 4: Deterministic Releases (The Gatekeeper) Respect the boundary between nondeterministic agents and production. Use a Release Gatekeeper Agent that doesn't deploy, but confirms all gates are passed (tests green, approvals present) and hands a "ready-to-deploy" report to a human for the final click. Move from reactive toil to proactive orchestration. Are you experimenting with agents in your pipeline yet? Share your experience below. 👇 #DevOps #SoftwareEngineering #AI #SDLC #AgenticAI #Automation #CTO

Most AI Agents don’t fail because of bad models They fail because their foundations are ignored Everyone obsesses over LLMs, tools, and benchmarks. Very few design agents like long-running systems. This is what actually breaks AI Agents in production 👇 📌 The most ignored foundations of AI Agents 1\ Tiered Memory Architecture - Treating memory as a single blob kills signal quality fast. - You need separation: a/ Short-term context (cache) b/ Episodic memory (recent tasks, interactions) c/ Long-term memory (validated knowledge)     Without pruning + decay, agents drift and hallucinate. 2\ Treating data as fuel, not logic - Agents don’t just *consume* data. - They reason *through* it. Bad data → warped decisions → cascading failures. Clean data pipelines matter more than prompt tuning. 3\ Fallback mechanisms - Production agents must fail under human guidence. - Not silently. Design explicit fallbacks when: - Tools fail - Data is missing - Reasoning paths collapse   Escalation > retries > safe defaults. 4\ Problem first, technology later - Agent sprawl happens when teams chase tech instead of outcomes. - No routing, no policies, no ownership = legal and reputational risk. Start with the problem. Then design the agent. 5\ Knowledge Graphs for context - Vector DBs help find information. - Knowledge Graphs help understand relationships, constraints, and causality. - Use vectors for retrieval. - Use graphs for reasoning. 6\ Policy enforcement in-flight - Governance isn’t a post-processing step. - Policies must constrain decisions *while* the agent is thinking. Not after the output is generated. 7\ UX for asynchronous autonomy - If users can’t see progress, agents feel broken. Status updates, iteration trails, and checkpoints matter more than chat UIs. 8\ Observability and success metrics Track what actually matters: - Task completion rate - Reasoning consistency - Hallucination rate - Tool success rate If you can’t observe it, you can’t improve it. This is why most “AI agents” look impressive in demos… and collapse in real workflows. 📌 and if you're like me, who likes to learn more from in-depth books on how to build scalable AI Agents My book will tell you all about that 📗 Book info: https://amzn.to/4irx6nI Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents