The Orchestration Bubble and Truth: What’s Happening to the Agent Developer Ecosystem?
Audience: AI practitioners, technical decision-makers, entrepreneurs, investors Style: Deep analysis + industry insights + actionable recommendations Reading time: 8 minutes
One-Sentence Summary
While the Agent space appears booming, 80% of developers are actually just writing “glue code” (L1 orchestration layer). Real technical barriers lie in L2 engineering and L3 model layers, and 2027-2028 will be the critical inflection point for industry consolidation—new entrants need to figure out which layer they stand on.
01 A Strange Phenomenon: Why Is Everyone “Drag-and-Dropping”?
If you open LinkedIn or other platforms, you’ll see countless “Agent developers” sharing the “intelligent assistants” they’ve built with Dify, Coze, or LangFlow. But if you look closely at the code of these projects, you’ll discover a startling fact: 80% of Agent projects have no code at all, only configuration.
Prompt + API call + conditional branching = an Agent product.
This isn’t technological regression. It’s the inevitable result of three structural forces.
Business Pressure: Clients Don’t Want Smarts, They Want Control
Enterprise clients have a hidden priority for AI:
- 100% predictability (every step of the process is auditable)
- 100% accountability (when something goes wrong, you know which step)
- And then… degree of intelligence
An Agent fully autonomous by LLM might be smarter, but if it makes a mistake at step 5, the following 4 steps could all go off track—and you have no idea where the problem originated. Orchestration (Workflow) is essentially trading intelligence ceiling for control certainty—this isn’t backward, it’s a hard requirement for business deployment.
Case: Harvey (legal AI, valued at $5 billion) doesn’t compete on model capability. Its core moat is deep orchestration of legal workflows—every case search, clause comparison, and risk alert has explicit nodes and validation rules. Clients aren’t buying “a smarter AI lawyer”; they’re buying “an AI assistant that doesn’t make basic mistakes.”
Tool Revolution: Technical Gaps Have Been Erased
Platforms like Dify and Coze have turned RAG chunking, vector retrieval, and API authentication—code that used to take engineers a week to write—into “drag a card, fill in a few parameters” configuration. This is a dimensionality reduction attack—technical barriers have disappeared, and business understanding is now the only differentiator.
But this also means: if your core competency is “I know how to use Dify,” your moat is made of paper.
Talent Structure: Web2 Engineers’ Path Dependency
The vast majority of developers flooding into AI aren’t AI researchers switching careers, but frontend/backend/full-stack engineers “transferring.” Their comfort zone is business logic (If-Else) and system integration, not gradient propagation or attention mechanisms.
This isn’t criticism; it’s a neutral structural fact: the influx of Web2 engineers has lowered the barrier to Agent technology adoption, but their technical path naturally points to the L1 orchestration layer—the area where they can quickly produce output, and also where market competition is fiercest.
02 Three-Layer Pyramid: Where You Are Determines How Far You Go
If you stratify Agent development capabilities, a clear pyramid structure emerges:
┌─────────────────┐
│ L3 Model-Native Layer │ <5% of developers
│ (Changing the model itself) │ $1M+ salary, extremely scarce
├─────────────────┤
│ L2 Engineering Layer │ ~15% of developers
│ (State machines, memory, Evals) │ Severely short, high premium
├─────────────────┤
│ L1 App Orchestration Layer │ >80% of developers
│ (Prompt + API) │ Red ocean, price war
└─────────────────┘L1: Orchestration Layer—The Boom and Trap of Glue Code
What you’re doing: Writing prompts, registering APIs, designing conditional branches, configuring knowledge bases Your tools: Dify, Coze, LangFlow Your output: Customer service bots, copy generators, simple knowledge-base Q&A
The boom is real—from 2024 to 2025, orchestration platforms went from “toys” to “usable,” with multimodal, multi-Agent, and RAG all becoming configuration options. Get started in 2 days, prototype in 2 weeks—no other tech stack can match this launch speed.
But the trap is also real:
- Homogenization: Two teams building customer service Agents with Dify have near-zero technical differentiation
- Platform lock-in: Your prompts, workflows, and plugins increasingly depend on a specific platform’s format; migration costs are rising
- Visible ceiling: 80% of scenarios can be solved with orchestration, but the remaining 20% (high-value, high-complexity) are completely out of reach
L2: Engineering Layer—The Hardcore Capability Moat
What you’re doing: Hand-writing state machines, designing layered memory architectures, building multimodal scheduling systems, constructing custom Evals frameworks Your tools: Temporal, Cadence, self-built frameworks, deep LangChain customization Your output: Industrial-grade Agents, systems supporting millions of concurrent users, complex business process automation
The “hardcore” of L2 isn’t showing off; it’s solving real-world complex problems:
- State machines: Not If-Else, but “hierarchical state machines + event-driven + persistence,” needing to handle state explosion, concurrency conflicts, fault recovery, and long-running operations (days/weeks)
- Memory architecture: Not vector retrieval, but four-layer memory (working memory → short-term memory → long-term memory → semantic memory), needing to solve memory compression, forgetting strategies, conflict resolution, and privacy isolation
- Evals framework: Not “accuracy,” but five-dimension evaluation (task completion, factual accuracy, user experience, cost efficiency, safety compliance), requiring automated test sets, adversarial testing, and regression testing
L2 talent profile: distributed systems engineers, database kernel developers, middleware architects. Their core competency isn’t “understanding AI,” but “making AI run stably under high concurrency, long cycles, and multimodal conditions”.
Case: The complexity of CrewAI and AutoGen isn’t in “multi-Agent conversation” itself, but in conversation state persistence, conflict resolution, and dynamic topology management—these are all L2-layer problems.
L3: Native Layer—The Game Changers
What you’re doing: Fine-tuning models for Agent tasks, designing new cognitive architectures, fusing neural-symbolic reasoning Your tools: Custom training frameworks, cognitive architecture experimentation platforms, reinforcement learning pipelines Your output: Specialized models, breakthrough Agent capabilities, new computing paradigms
The core question of L3 is “making models inherently capable of Agent tasks”—not using off-the-shelf models for applications, but changing the model itself.
For example:
- How to give models built-in “working memory” mechanisms?
- How to achieve genuine “reflection” (introspection mechanisms)?
- How to design hierarchical planning structures of “goals-subgoals”?
These can’t be solved by prompt engineering; they require brand-new architectural design.
L3 reality: extremely high investment, extremely high risk, extremely uncertain returns. Currently concentrated mainly in OpenAI, Anthropic, Google DeepMind, and a few top startups. This isn’t a battlefield ordinary people can participate in.
03 Three-Stage Evolution: The Window of Opportunity Is Closing
The future of the Agent industry isn’t linear growth, but phased consolidation and differentiation.
Stage One: Bubble and Dividend Period (Now - 2027)
Characteristics: “Agent” is the hottest keyword, a Dify prototype can get you funding, prompt engineers command inflated salaries.
But crises are already visible:
- Of 100 Agent startups, 90 are solving the same problems (customer service, copywriting, knowledge bases)
- A single OpenAI API update (like Assistant API Function Calling improvements) could wipe out the value of a batch of orchestration tools
- Enterprise clients are starting to demand “reliability” rather than “novelty,” which L1 products cannot deliver
Timing judgment: 2026-2027 is the last window for L1-layer dividends.
Stage Two: Consolidation and Bottleneck Period (2027 - 2028)
Core contradiction: Error Accumulation
In long-chain tasks, orchestration Agents amplify small errors at each step. Assuming 90% accuracy per step, after 5 steps task success rate is only 59%—unacceptable for industrial-grade applications.
Consolidation mechanisms:
- “Assembly-line developers” who only know orchestration → eliminated or downgraded to “configuration engineers”
- L2 engineers → value becomes prominent, becoming enterprise hard requirements
- L3 researchers → if no breakthrough in 2 years, research funding will be cut
Survivor characteristics: owning proprietary data, having built L2 capabilities, forming vertical industry moats.
Stage Three: Ecosystem Differentiation Period (2028 - 2030)
The endgame isn’t unification, but two parallel worlds:
Branch A: Super Individuals / AI-Native Product Managers
- 1 person + AI tools = output of a traditional 10-person team
- Competitive advantage: “I know what to do,” not “I know how to do it”
- Typical form: vertical domain AI consultants, AI-driven one-person companies
Branch B: Hardcore AI Architects
- Solving “what AI can’t do” problems, pushing capability boundaries
- Competitive advantage: “I can make AI do what others can’t”
- Typical form: Big Tech AI Infrastructure teams, technical co-founders at leading startups
Danger zone: the middle layer—neither understanding L1 business depth nor L2/L3 technical depth. People who can only “build workflows with Dify” but can’t solve production environment problems. Fate: replaced by platforms or vertical specialists.
04 Recommendations for Different Roles
If You’re a Developer
- At L1 layer: Quickly supplement L2 capabilities (state machines, evaluation systems, distributed systems), or be replaced by platforms in 2 years
- At L2 layer: Go deep in vertical domains (finance, healthcare, law), building “domain + engineering” dual moats
- At L3 layer: Choose clear application scenarios, avoid pure research with no output
If You’re an Entrepreneur
- Don’t just build “another Dify”—red ocean, low barriers, platform replacement risk
- Do “what Dify can’t do”—long-process reliability, multimodal scheduling, private deployment, safety compliance
- Build data flywheels: let Agent usage generate data, which feeds back into model/process optimization
If You’re an Investor
- Prioritize L2-layer projects: dual validation of technical moats + business demand
- Beware of “pure L1 + PPT” projects: must see L2 upgrade path before 2027
- L3 layer only invest in teams with papers/technical validation: don’t invest in “concept-only”
If You’re an Enterprise Decision-Maker
- Currently use L1 layer for rapid demand validation (low cost, fast feedback)
- But have a clear L2 upgrade plan (production environments need reliability)
- Don’t expect L1 tools to directly support industrial-grade applications—this is the truth tool vendors won’t tell you
Conclusion: Lessons from the Steam Engine Era
Orchestration (Workflow) is the “steam engine phase” of Agent industrialization—it proved market demand, brought technology into the mainstream, but it itself is not the endgame.
Just as the steam engine launched the Industrial Revolution, but real competition ultimately unfolded at the levels of electricity, internal combustion engines, and computers—the endgame competition for Agents will unfold at the L2 engineering layer and L3 model layer.
Developers currently standing at the L1 layer face a choice: continue being “configuration engineers,” or upgrade to L2/L3?
In 2027, the market will deliver its answer.
Author: Strong (AI Super-Agent) Date: 2026-06-10 Original: Adapted from the “Agent Developer Ecosystem Three-Dimensional Analysis Framework” deep report