Uploaded July 2026 | Updated September 2026, 2 weeks ago
What comes after AI coding agents?
In this Observe 2026 session, Eno Reyes, CEO and cofounder of Factory AI, explains the rise of the “software factory”: a new model for software development where AI agents operate across the entire SDLC — from triage and planning to code review, QA, deployment, monitoring, and incident response.
Eno walks through how AI software development has evolved from autocomplete to coding agents, why enterprises need to think beyond individual tools, and what it takes to build agent-ready engineering systems that improve cycle time without increasing risk.
You’ll learn:
- Why AI coding agents are only one stage in the evolution of software development
- What a “software factory” is and how it changes the SDLC
- Why cost, model access, governance, and developer roles become urgent at enterprise scale
- Why model-agnostic AI infrastructure matters
- How agents can support code review, QA, security analysis, documentation, incident response, and deployment
- Why codebases need deterministic signals like tests, linters, type checks, and formatting
- How the role of the software engineer shifts from building code to stewarding an AI-driven system
- Why teams should measure outcomes like cycle time, bugs, and incidents — not tokens or lines of code
Chapters:
0:00 The rise of the software factory
0:49 How AI software development keeps changing
1:33 From autocomplete to coding agents
2:03 What comes after coding agents?
2:17 The urgent enterprise questions
3:47 Why bottlenecks kill software ROI
4:40 The SDLC as a feedback loop
5:26 AI agents across the entire software lifecycle
6:08 Why model-agnostic AI matters
6:55 Owning your software factory
7:32 Canonical agents across the SDLC
7:59 One agent across every surface
9:12 Governance, audit, and enterprise controls
10:20 Getting your codebase agent-ready
11:04 Agent readiness: acceleration vs. deceleration
11:49 The engineer as a software factory steward
12:43 Measure outcomes, not tokens
13:15 The self-driving car analogy for AI agents
13:53 Bugs and incidents as the key metric
14:45 Building the software factory journey
Presented at Observe 2026.
#AIAgents #FactoryAI #SoftwareEngineering
🔗 Try Arize AX & Phoenix OSS: arize.com
🔔 Subscribe for weekly content on LLMs, agents, and evaluation: youtube.com/@arizeai?sub_confirmation=1
What comes after AI coding agents?
In this Observe 2026 session, Eno Reyes, CEO and cofounder of Factory AI, explains the rise of the “software factory”: a new model for software development where AI agents operate across the entire SDLC — from triage and planning to code review, QA, deployment, monitoring, and incident response.
Eno walks through how AI software development has evolved from autocomplete to coding agents, why enterprises need to think beyond individual tools, and what it takes to build agent-ready engineering systems that improve cycle time without increasing risk.
You’ll learn:
- Why AI coding agents are only one stage in the evolution of software development
- What a “software factory” is and how it changes the SDLC
- Why cost, model access, governance, and developer roles become urgent at enterprise scale
- Why model-agnostic AI infrastructure matters
- How agents can support code review, QA, security analysis, documentation, incident response, and deployment
- Why codebases need deterministic signals like tests, linters, type checks, and formatting
- How the role of the software engineer shifts from building code to stewarding an AI-driven system
- Why teams should measure outcomes like cycle time, bugs, and incidents — not tokens or lines of code
Chapters:
0:00 The rise of the software factory
0:49 How AI software development keeps changing
1:33 From autocomplete to coding agents
2:03 What comes after coding agents?
2:17 The urgent enterprise questions
3:47 Why bottlenecks kill software ROI
4:40 The SDLC as a feedback loop
5:26 AI agents across the entire software lifecycle
6:08 Why model-agnostic AI matters
6:55 Owning your software factory
7:32 Canonical agents across the SDLC
7:59 One agent across every surface
9:12 Governance, audit, and enterprise controls
10:20 Getting your codebase agent-ready
11:04 Agent readiness: acceleration vs. deceleration
11:49 The engineer as a software factory steward
12:43 Measure outcomes, not tokens
13:15 The self-driving car analogy for AI agents
13:53 Bugs and incidents as the key metric
14:45 Building the software factory journey
Presented at Observe 2026.
#AIAgents #FactoryAI #SoftwareEngineering
🔗 Try Arize AX & Phoenix OSS: arize.com
🔔 Subscribe for weekly content on LLMs, agents, and evaluation: youtube.com/@arizeai?sub_confirmation=1

![When AI Can Write Code, What Are Software Engineers Worth? | Citadel
When AI can generate code in seconds, what still makes a software engineer valuable?
In this Arize:Observe session, Craig Owenby of Citadel explores how agentic coding tools are changing software engineering, and why the profession still requires far more than producing code.
Craig compares large language models to the printing press. The printing press replaced the manual work of copying books, but it did not replace authors. In the same way, AI coding agents can automate the mechanics of writing code without replacing the judgment, vision, empathy, and experience required to build useful software.
The session covers:
• Why “coder” and “software engineer” are increasingly different roles
• How tools like Claude Code, Codex, Copilot, and Cursor remove traditional barriers to building software
• What the printing press teaches us about AI-assisted development
• Why engineers should avoid competing with AI on raw code generation
• How Sears lost its advantage by competing with e-commerce on the wrong terms
• Why human experience, intuition, and empathy remain essential
• How constraints can improve product and engineering decisions
• Why shipping more features can increase volatility and reduce user trust
• How AI acts as leverage for strong and weak engineering decisions
• Why product direction and problem selection matter more as implementation gets easier
The central lesson: software engineering is not primarily about writing code. It is about deciding what should be built, understanding why it matters, and applying technology with judgment.
When everyone can code, engineers differentiate themselves through their standards, instincts, product sense, and ability to understand the people using what they build. :contentReference[oaicite:0]{index=0}
Chapters:
00:00 When everyone can code, what are engineers worth?
00:46 LLMs and the printing press
01:30 The limits that shaped software engineering
02:42 We finally live in a world where everyone can code
03:45 The existential question for experienced engineers
04:45 Coders versus software engineers
06:05 Don’t make the same mistake as Sears
07:38 Human judgment, experience, and empathy
09:02 Why constraints can produce better software
10:25 Product volatility and the Sharpe ratio
11:49 Software engineering is about solving problems
12:38 LLMs as leverage for engineers
13:45 What sets engineers apart
14:25 Your humanity is the key
🔗 Learn more about Arize: https://arize.com
🔗 Explore Arize:Observe: https://arize.com/observe
🔔 Subscribe for more talks on AI engineering, coding agents, evaluation, observability, and the future of software development:
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#SoftwareEngineering #CodingAgents #AIEngineering When AI Can Write Code, What Are Software Engineers Worth? | Citadel](https://i.ytimg.com/vi/TllPOmVWF8s/mqdefault.jpg)








