Reddit AI Agent — 2026-04-12¶
1. What People Are Talking About¶
1.1 Over-Engineering vs. Simplicity (🡕)¶
The loudest signal across today's 165 posts is a growing backlash against unnecessary complexity in agent development. Practitioners are calling out "framework cosplay" — using heavyweight orchestration frameworks for tasks that a short script would handle better — and arguing that the agents actually generating revenue are boring, single-purpose, and invisible.
u/Mental_Push_6888 sparked the day's top discussion (S77, 76 comments) asking why developers keep reaching for LangGraph or CrewAI when a 50-line Python script would suffice. The post draws a clear line: agents earn their complexity only when they need dynamic mid-execution decision-making, tool chaining that depends on prior results, multi-turn state, or truly unpredictable input. Everything else is "an API wrapper with extra steps and 10x the latency" (Why do people keep using agents where a simple script would work?).
Discussion insight: u/ash286 (S60) quipped "how would they post a LinkedIn post about it if they just wrote a script?" — capturing the social-media-driven incentive to over-engineer. u/HaremVictoria (S25) coined "framework cosplay" and noted that half their consulting work is talking clients out of building agents. u/Comedy86, a 20-year software veteran, pushed further: three of the four criteria the OP listed as requiring agents do not actually require AI at all — they are standard business logic, state management, and structured decision trees that software has handled for decades.
u/Admirable-Station223 reinforced this from the business side, arguing that 90% of agents being built will never earn a dollar. The real money is in "boring" single-step AI tasks plugged into established workflows — reading a company's website to write one personalized sentence, sorting email replies into buckets, extracting intent signals from job postings (90% of AI agents being built right now will never make a dollar). u/DramaLlamaDad pushed the estimate further: "More like 99.9% don't make money."
Comparison to prior day: The over-engineering sentiment has been building for several days, echoing the prior day's top discussion (S1080) about Anthropic and current tooling.
1.2 The Production Readiness Gap (🡕)¶
A constellation of posts document the same pattern: agents and workflows that perform well in demos collapse under real-world conditions. The gap between demo and production dominates at least six posts today.
u/EveningWhile6688 directly asked where agents break in production (S16, 32 comments). Respondents identified specific failure modes: u/Icy_Host_1975 pointed to "state and control-plane drift: auth expires, tools return partial success, background jobs outlive the user context." u/RegularOk1820 estimated that "unexpected user behavior is like 80% of it" (Where are your agents actually breaking in production?).
u/akhilg18 captured the frustration memorably: "the more 'autonomous' we try to make it, the more guardrails we end up adding. At some point it doesn't even feel autonomous anymore, just controlled chaos" (Are we building agents... or just babysitting them?). u/Dailan_Grace diagnosed the root cause as a systems problem, not a model problem — "the demo environment is basically a controlled fantasy" (why AI demos look amazing and then fall apart the moment you ship).
On the n8n side, u/Annual_Ad_8737 asked what breaks first when moving workflows to production. u/pvdyck described "silent data corruption" — a third-party endpoint dropped a field without warning, and the workflow kept running with wrong data going into the CRM for three days before anyone noticed (What actually breaks first when you move n8n workflows to production?).
Comparison to prior day: Production breakdowns were also discussed yesterday (e.g., "Where are your agents actually breaking in production?" appeared on both days), but today's dataset is richer in specific failure categories and practitioner detail.
1.3 n8n as the Dominant Automation Platform (🡒)¶
n8n appears in 13 of the top 83 posts and is mentioned in discussions across subreddits well beyond r/n8n. The platform is being used for everything from news digests to content strategy to PDF automation.
u/Professional_Ebb1870 delivered the day's most insightful n8n post (S66): "production workflows aren't linear. They're state machines." Three hard-won insights after two years: n8n is event-driven not flow-driven, the canvas is just a visualization (not the source of truth), and debugging — not building — is the actual skill that separates hobbyists from production-grade builders (what nobody tells you before you start with n8n). u/ecompanda confirmed: "the state machine framing is the mental shift that makes everything click."
The same author also shared a 57-node X content strategy bot with a self-critique loop — a second AI agent reviews generated posts against quality criteria before publishing, with retry logic and Telegram notifications on skip (I stopped manually posting on X and built a bot that judges my content before it posts). The full workflow suite is open-sourced on GitHub.
1.4 Learning Paths and the Skills Gap (🡕)¶
Demand for structured guidance on agent development is rising, driven both by newcomers and by professionals who find themselves in roles that require automation expertise they do not yet have.
u/ahmedhashimpk asked for a learning roadmap (S41, 31 comments). The top comment (S58) from u/Pitiful-Sympathy3927 delivered a detailed, contrarian roadmap: skip no-code initially, learn what an LLM actually is ("It is a next-token predictor. It does not think"), learn Python, master function calling ("the single most important concept in agent development"), build a small real project, study failure modes, and understand observability. The comment explicitly dismisses prompt engineering certifications and YouTube tutorials: "The shortest honest path: learn Python basics, learn function calling, build a small real project, break it until you understand why it breaks" (Learning roadmap for AI Agent development).
u/Novel-Marionberry661 provided a vivid counterpoint: hired as an executive assistant with emphasis on AI automation, they oversold their capabilities in the interview and now have 90 days to automate an entire business. The post attracted 69 comments, many offering practical advice. u/mrmigu (S74) responded: "You said you know how to use ChatGPT, get it to teach you" (I got hired to Automate workflows for the business and I don't know what to do).
u/Striking_Table1353 asked about the concept of "skills" in AI agents (S21). u/tacit7 (S10) provided the clearest definition in the dataset: skills are specialized markdown files following the Agent Skills open standard, enabling custom commands, context management, and domain expertise without bloating the base context window (Can someone explain what skills are and how they work?).
1.5 Agent Architecture Debates (🡒)¶
How to structure multi-agent systems remains an active design question with no consensus.
u/Distinct-Garbage2391 framed the core dilemma: one highly trained LLM with 100 tools, or 20 specialized agents talking to each other? u/Exact_Guarantee4695 reported from production: "the sweet spot ended up being a dumb routing layer that dispatches to specialized agents. Once your router starts reasoning about which agent to pick you're back to square one on token costs." u/Deep_Ad1959 added a constraint: for desktop app interactions where one agent clicking a button changes what another sees, a swarm creates coordination problems that messaging protocols cannot solve fast enough (Master Agent or Swarm of Micro-Agents?).
u/jkoolcloud identified what they called the most common architectural mistake in agentic deployments: one agent run can touch multiple models, tools, workers, and tenants, but the controls are local and fragmented. "Provider caps, observability, framework limits, and Redis counters all help, but none really answers: can this agent, for this customer, on this worker, take the next action right now?" (The architectural mistake I keep seeing in agentic deployments).
u/Total-Hat-8891, responding to an architecture question, laid out the most concrete stack recommendation: Vercel frontend, FastAPI or Node API (stateless on Cloud Run/Railway/Fly), Postgres for data, Redis for session state and queues, object storage for files, and orchestration (LangGraph or Temporal) only when genuinely needed. The key insight: "Do not start with a multi-agent architecture just because that is what people post online."
1.6 The AI Agency Client Problem (🡒)¶
Multiple posts from automation builders and aspiring agency owners converge on the same question: where do you find paying clients?
u/Agnostic_naily asked directly: "I make AI agents, but I struggle to get clients" (How to get clients?). u/Lost_Budget_7355 asked what automations businesses actually pay for (What automations will businesses actually pay for?). u/Mysterious-Catch-182 sought an automation expert for cold email and lead generation (Looking for an AI automation expert).
The pattern across responses: stop building features, start solving specific pain points for specific businesses. u/sanchita_1607 summarized: "the tech is the easy part. The hard part is finding a problem specific enough that a business will actually pay to solve it."
2. What Frustrates People¶
Production Failures and Silent Breakdowns — High¶
The most pervasive frustration is the gap between demo performance and production reliability. Multiple posts document specific failure modes:
- Silent data corruption: u/pvdyck described a workflow running clean for two months until a third-party endpoint silently dropped a field. The workflow kept executing with wrong data going into the CRM for three days. "A hard crash is honestly easier to catch than a workflow that runs perfectly and quietly ruins your dataset" (What actually breaks first when you move n8n workflows to production?).
- State and control-plane drift: Auth tokens expiring, tools returning partial success, background jobs outliving user context. These failures hide because demos run in short, clean loops (Where are your agents actually breaking in production?).
- Unexpected user behavior: u/RegularOk1820 estimated this accounts for 80% of production failures — "people don't follow flows at all, they just mash random stuff and expect magic."
- Agent babysitting overhead: The "more guardrails we add, the less autonomous it feels" loop identified by u/akhilg18 is widespread. The real engineering work is happening outside the agent — validation, retry logic, output checking — not inside it (Are we building agents... or just babysitting them?).
People cope by adding idempotency checks, dedicated error sub-workflows with Slack alerts containing exact failed payloads, SQL-based state storage outside n8n, and strict input shape validation. But these are manual engineering solutions applied case by case, with no standardized tooling.
Token Cost Blowouts — Medium¶
u/JosetxoXbox provided the most concrete cost data: an n8n SEO workflow updating 1,000+ blog posts costs $0.25 per article, with the competitor analysis node consuming most of the budget by passing full web pages through the context window. The target is $0.10 per article, but current architecture cannot achieve it without fundamental redesign (High Token Costs ($0.25/art) in n8n SEO Workflow). u/Idiopathic_Sapien noted in the architecture thread: "I like the swarm concept but the token usage gets crazy."
Opaque Usage Quotas — Medium¶
u/General-Tip-4727 cataloged frustrations across multiple platforms: Claude Code with non-transparent usage metrics and on-the-fly rate limit changes, GitHub Copilot "nerfing day by day" with hidden rate limits and failed requests that still eat credits, and Google Antigravity with wrong quota displays. The frustration is that users are paying for "premium" tools but cannot predict or control their costs (Unclear Usage Quotas of AI Agents).
Brand Voice Dilution — Medium¶
u/Daniel_Janifar identified a recurring problem with AI email tools: drafts are solid 80% of the time, but the other 20% "sounds like it was written by a press release." The bigger trap is letting AI "sand down all the personality until it sounds like every other corporate newsletter." Workarounds include maintaining per-client "voice docs" with specific phrases, anti-patterns, and punctuation habits (how are small businesses actually handling AI email tools without losing their voice).
Fragmented Cross-Cutting Controls — Low¶
u/jkoolcloud described an architectural frustration: when an agent spans multiple LLMs, tool calls, and providers, there is no unified decision layer to answer "can this agent, for this customer, take this action right now?" Provider caps and observability tools each govern one slice, but none cover the full runtime surface (The architectural mistake I keep seeing in agentic deployments).
3. What People Wish Existed¶
Agent Evaluation Tooling That Non-Engineers Can Use¶
u/Kind-Ad4597 described being stuck in "Excel Hell": running batch evaluations, exporting reasoning steps and outputs to Google Sheets, then emailing them to domain experts who "are expensive, busy, and absolutely hate spreadsheets." The HITL (Human-in-the-Loop) evaluation loop is the bottleneck, and no existing tool adequately bridges the gap between agent output and domain expert review (Anyone else stuck in "Excel Hell" trying to get domain experts to evaluate agent outputs?). This is a practical need with clear economics — it directly constrains how quickly agent systems can iterate.
Seamless Integration Layer ("The Last Mile")¶
u/Icy-Maintenance-5962 articulated a widely felt gap: the ability to say "build this" in plain English is basically here, but the friction of setting up accounts, connecting APIs, dealing with auth, and moving data around breaks the illusion. "Feels like the last mile is just stitching everything together cleanly without the human glue in the middle." u/mlueStrike (S7) pushed back: "We're not 6 months away from full autonomy. Half the 'build this' ops that work are super simplistic things" (We're so close...). Partially addressed by MCP and tools like OpenTabs, but no comprehensive solution exists today.
Cross-Platform Context Portability¶
u/114514onReddit described the pain of switching between AI platforms (OpenAI, Anthropic, Gemini) without losing chat history and context. Current solutions either lose history entirely or use a small model to summarize, which loses too much context for the new model to work well (How to switch between AI platforms and not losing chat history/context).
Persistent Agent Memory That Actually Works¶
Multiple posts reference memory as a fundamental gap. u/ultrathink-art described building a two-tier memory system (hot markdown files for recent context, SQLite with semantic embeddings for long-term recall) because "agents re-learn the same lessons every session." The deduplication step matters more than storage itself — without cosine similarity filtering, retrieval quality collapses. u/no_oneknows29 said simply: "make sure your agent has good and persistent memory."
AI Agents for System Administration¶
u/Sova_fun noted that while everyone talks about agents for coding, very few address system administration — managing bare-metal servers, networking devices, switches, and routers. The sysadmin domain remains largely unserved by current agent tooling (AI agents for sysadmins?).
4. Tools and Methods in Use¶
| Tool | Category | Sentiment | Strengths | Limitations |
|---|---|---|---|---|
| n8n | Workflow automation | (+) | Self-hosted, event-driven, strong community, visual canvas, free tier | Steep learning curve for production, debugging is hard, no built-in state persistence, Docker image changes break workflows |
| Claude Code | Coding agent | (+/-) | Strong reasoning, function calling, skills system | Non-transparent usage quotas, rate limit changes without notice |
| OpenClaw | Agent framework | (+/-) | Multi-agent system, Discord/Telegram integration, Obsidian memory, community guides | Buggy, heavy for simple use cases, "can't be buggier if they tried" |
| LangGraph | Orchestration | (+/-) | DAG-based, state machines | Over-used for simple problems, adds latency and complexity |
| CrewAI | Multi-agent | (+/-) | Multi-agent pipelines, role-based | Often unnecessary for single-step tasks, contributes to over-engineering |
| MCP (Model Context Protocol) | Integration protocol | (+) | Standardized tool connections, growing ecosystem | Security concerns (no per-agent access controls without additional tooling) |
| Hermes | Agent framework | (+) | Single-assistant focus, stable for personal use | Less suited for multi-agent scenarios |
| Make/Zapier | Workflow automation | (+/-) | Easy to start, large integration library | Limited for complex workflows, per-execution pricing at scale |
| ChatGPT/GPT-4 | LLM | (+/-) | Widely available, function calling support | Context degradation over time, rewrites without asking |
| Gemini | LLM | (+/-) | Good for summaries and translation, free tier | Inconvenient interface, fixates on random words, interface limitations |
| Airtable | Database/CRM | (+) | Good for storing workflow state and content data | Used as a crutch for state management that should be in SQL |
| jina.ai | Content extraction | (+) | Converts web pages to clean markdown, reduces token cost 60-70% | Limited adoption awareness |
Overall satisfaction spectrum: n8n has the strongest community loyalty but draws the sharpest criticism about the tutorial-to-production gap. Claude Code is respected for capability but distrusted on pricing transparency. OpenClaw generates the most tutorial content but also the most bug complaints.
Migration patterns: Several users describe moving from Make/Zapier to n8n for self-hosting and flexibility. A notable emerging pattern is practitioners avoiding full agent frameworks entirely and using Claude Code + MCP + n8n as a lightweight alternative.
Workarounds: For token costs: jina.ai to strip HTML to clean markdown (60-70% reduction), Serper.dev instead of LLM-driven search, heading-only extraction. For brand voice: per-client "voice docs" with specific phrases and anti-patterns pasted into every prompt.
5. What People Are Building¶
| Project | Who built it | What it does | Problem it solves | Stack | Stage | Links |
|---|---|---|---|---|---|---|
| Daily News Digest RSS | u/MohannadMadi | Pulls RSS feeds, groups by topic, generates email digest | 9+ hrs/week doomscrolling on X | n8n, Gemini, RSS | Shipped | GitHub |
| X Content Strategy Bot | u/Professional_Ebb1870 | 57-node n8n workflow with self-critique loop for automated X posting | Repetitive content creation, quality inconsistency | n8n, Claude Code, Synta MCP, Airtable, OpenRouter, Apify, Telegram | Shipped | GitHub |
| Agent Mailer Protocol (AMP) | u/Negative-Border1439 | Email-like async messaging for AI agent collaboration | Agents can't talk to each other without DAGs or message queues | Python, FastAPI, PostgreSQL, JWT, Docker | Shipped | GitHub |
| MCP Harbour | u/ismaelkaissy | Security proxy between agents and MCP servers with per-agent policies | No access control in MCP — agents get full access to everything | Go (binary), GPARS spec | Shipped | GitHub |
| Surogates | u/deepnet101 | Multi-tenant managed agent platform with brain/hands separation | Enterprise agent orchestration at scale | FastAPI, model-agnostic, durable sessions | Alpha | GitHub |
| Engram Translator | u/Mobile_Discount363 | Semantic interoperability layer with auto-generated tool schemas | Brittle tool integrations that break when APIs drift | Python, OWL + ML, MCP/CLI/A2A/ACP | Alpha | GitHub |
| PDF E-Sign Workflow | u/Few-Peach8924 | Drop PDF in folder, auto-sign, email or Drive upload | Manual document signing workflow | n8n, Google Drive, PDF API Hub | Shipped | GitHub |
| Science Radar | u/emmecola | 9-agent pipeline drafting illustrated science essays | Staying current on scientific topics | CrewAI, Codeberg | Alpha | Codeberg |
| OpenTabs | u/opentabs-dev | MCP server routing AI tool calls through browser sessions | API key and OAuth setup for every service | Node.js, Chrome extension, MCP | Shipped | GitHub |
| AutoRewarder v3.0 | u/18safarov | Microsoft Rewards automation with Bezier curve mouse physics | Manual rewards collection | Python, Playwright | Shipped | Reddit post |
| AI Agent Orchestrator | u/WabbaLubba-DubDub | DAG-like orchestration for AI agents with MCP tools | Multi-step agent task management | MCP, DAG orchestration | Alpha | Reddit post |
| Enterprise AI Use Case Catalog | u/AffectionateGuava238 | 35+ documented enterprise AI use cases with full design specs | No structured reference for enterprise agent implementations | Web | Shipped | Site |
| Persistent Entity | u/Icy-Ebb9716 | Sandboxed agent that writes a diary and passes memories to next run | Rigid agents losing state between sessions | Sandbox, diary persistence | Alpha | Reddit post |
Agent Mailer Protocol takes a distinctive approach: instead of DAGs or workflow engines, agents communicate through email-like semantics — inbox, send, reply, forward, and threading. The author reports 17 agents running across 5 teams. It addresses the coordination problem by providing loose coupling through async messaging, compatible with Claude Code, Cursor, and custom frameworks.
MCP Harbour fills a specific security gap: when you give an agent access to an MCP server, it gets access to everything. MCP Harbour enforces per-agent security policies, built as a GPARS specification implementation.
Repeated build pattern: Three independent projects (AMP, Surogates, DAG orchestrator) all address multi-agent coordination, suggesting the pain point is severe enough to motivate parallel development. Two projects (Engram, MCP Harbour) add governance layers on top of MCP, indicating secondary tooling demand from MCP adoption.
6. New and Notable¶
The Synthetic User Gap¶
u/Lopsided-Fan-9823 posted a research-dense analysis: a four-level taxonomy of persona simulation, from system-prompt wrappers (level 1) through multi-agent simulation (level 3) to Stanford's validated digital twins achieving 85% replication accuracy using RAG-grounded agents built from 2-hour interviews (level 4). Most commercial SaaS tools operate at level 1-2 while marketing as level 4. Stanford's research shows agents built from interview data outperform demographic-only agents by 14-15 percentage points. MiroFish (33k+ GitHub stars, ~$4M seed in 24 hours) sits at level 3 but has no benchmarks against actual outcomes (Most "synthetic user" AI tools are just ChatGPT with a system prompt).
Hardware + Agent Decoupling at REDHackathon¶
u/NOT_ARGHA highlighted a project from the REDHackathon in Shanghai: a "focus toaster" desktop device that photographs the user working and prints thermal receipts of their timeline. The interesting architectural decision is decoupling vision processing from the agent loop — the hardware handles capture and the agent handles reasoning independently. The author noted that "90% of the hardware track is just an API wrapper duct-taped to a Raspberry Pi," but this project's separation of concerns was "kinda changing how I look at embodied setups" (hardware hackathon projects but this repo's approach to decoupling vision from the agent loop is pretty solid).
Markdown-Based Agent Workflows as RFC¶
u/Defiant_Fly5246 proposed defining agent workflows as plain Markdown files rather than visual DAGs or code. The RFC drew 22 comments — substantial engagement for a score-3 post — suggesting the idea of radical simplification resonates even if the specific approach is debatable (RFC: What if AI agent workflows were just Markdown files?).
7. Where the Opportunities Are¶
[+++] Production Observability and Debugging Tools for Agent Systems — The production readiness gap is the most consistently documented pain point across subreddits. Silent data corruption, state drift, partial API failures, and the "babysitting" problem all point to a market gap: there is no standardized way to monitor, debug, and maintain agent systems in production. u/pvdyck's three-day silent corruption incident and u/jkoolcloud's fragmented-controls observation both indicate that existing APM tools do not map well to cross-cutting agent architectures. This is the infrastructure layer that would unlock wider production deployment.
[+++] Agent Evaluation Tooling for Domain Experts — The "Excel Hell" problem described by u/Kind-Ad4597 represents a clear, immediately addressable market. Agent builders need domain experts to evaluate outputs, but the tooling forces non-technical evaluators into spreadsheets. A purpose-built HITL evaluation interface — with annotation workflows, disagreement tracking, and automated iteration triggers — would directly accelerate agent development cycles. The constraint is well-defined and the buyer (agent development teams) is identifiable.
[++] MCP Security and Governance Layer — MCP Harbour's emergence confirms that as MCP adoption grows, the lack of per-agent access controls is becoming a real problem. The GPARS specification is early but already has an implementation. Any team building MCP-dependent systems will eventually need policy enforcement, audit logging, and tenant isolation — especially in enterprise contexts where u/jkoolcloud's multi-tenant control problem is most acute.
[++] Boring Single-Step AI Automation Services — u/Admirable-Station223's argument that revenue comes from boring, single-step AI tasks (personalized outreach sentences, email classification, intent extraction) is supported by multiple posts. The OpenClaw setup guide from u/Prentusai prices done-for-you automation builds at $2,000-$10,000 per client. The market is clients who know they need automation but lack the skill to build it — evidenced by posts seeking automation experts for cold email and lead generation.
[++] Token Cost Optimization Middleware — With concrete data points ($0.25/article dropping to $0.10 through context optimization) and multiple workarounds being shared (jina.ai, heading-only extraction, separating search from analysis), there is clear demand for a middleware layer that automatically optimizes context windows before LLM calls. Current solutions are all manual, per-workflow hacks.
[+] Cross-Platform Agent Context Portability — The desire to switch between AI providers without losing context is currently unserved. With multiple LLM providers competing on price and capability, users increasingly want to move between them mid-project. The persistent memory projects (two-tier storage, diary-based entity) are early attempts at solving part of this from the agent side.
[+] AI Agents for System Administration — u/Sova_fun identified a domain where agent tooling is almost entirely absent: managing bare-metal servers, networking equipment, and infrastructure. Unlike coding agents, this domain has fewer existing solutions and potentially higher willingness-to-pay from enterprise IT departments.
8. Takeaways¶
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The community is converging on "simplicity wins." The day's top post (S77) and its surrounding discussion establish strong consensus: most agent projects are over-engineered, and the real test is whether removing the LLM loop breaks the product. Practitioners who are actually making money are using boring, single-step AI tasks, not multi-agent orchestration. (Why do people keep using agents where a simple script would work?)
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Production reliability is the primary technical bottleneck. Silent data corruption, state drift, auth expiration, and unexpected user behavior are the dominant failure modes. The irony captured by u/akhilg18 — "the more autonomous we try to make it, the more guardrails we add" — defines the current state of agent engineering. (Are we building agents... or just babysitting them?)
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n8n's mental model shift from flowchart to state machine separates beginners from production builders. u/Professional_Ebb1870's framing — "production workflows aren't linear, they're state machines" — was the single most validated insight in the n8n community today. (what nobody tells you before you start with n8n)
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The MCP ecosystem is generating secondary tooling demand. Two independent projects (MCP Harbour for security, Engram for interoperability) address governance gaps in the MCP protocol, and MCP-based integrations appear in multiple build posts. The protocol is reaching the adoption level where missing layers become visible. (MCP Harbour, Engram)
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The agent learning roadmap is being rewritten by practitioners, not courses. The highest-signal learning resource in today's data is a Reddit comment (S58) that explicitly dismisses prompt engineering certifications, YouTube tutorials, and no-code tools as starting points. The recommended path: understand what an LLM is, learn function calling, build something real, and study failure modes. (Learning roadmap for AI Agent development)
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Multi-agent coordination is the most independently reinvented wheel. Three separate projects (AMP, Surogates, DAG orchestrator) all tackle agent-to-agent communication from different angles — email semantics, enterprise durable sessions, and visual DAG flows — suggesting no existing solution is adequate. (We built a mail protocol for AI agents)
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Demand for automation skills exceeds supply. A person got hired to automate an entire business based on claiming familiarity with ChatGPT, received 69 comments of advice, and illustrates a market where businesses are actively hiring for AI automation competence but the talent pool is shallow. (I got hired to Automate workflows for the business and I don't know what to do)