AI Engineer · Published 2026-10-07

The Software Factory: From Bug Report to Production Code — Davis Palmie, Factory

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Software FactoryAI agentssdlcagent readinessgovernancesoftware-factoryagent-readinessmodel-routingoutcome-metricsmulti-agentsovereign-deployment
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Summary

Overview

  • Speaker: Davis Palmie
  • Channel: AI Engineer
  • Main topic: Transitioning from coding agents to an autonomous Software Factory system
  • Purpose: Explain how engineering organizations can shift from manual coding and individual agents to a cohesive Software Factory system of agents that handle end-to-end SDLC workflows. Davis Palmie discusses the evolution of AI in software engineering, moving from autocomplete and coding assistants to fully autonomous software factories where AI agents handle the complete software development lifecycle from input signals to production deployment, while emphasizing agent readiness, governance, and outcome-based measurement.

Topic Map

The Evolution of AI in Engineering

  • Explanation: Tracing the progression from autocomplete to coding agents and finally to the Software Factory model.
  • Key claims:
    • AI engineering has moved through three distinct eras: autocomplete, coding agents, and software factory.
    • Each move up the abstraction ladder has made engineers more valuable, not less.
    • Engineers are moving from typing code to governing agents.
  • Examples:
    • Tab autocomplete -> Next token prediction -> Entire file generation -> Fully coupled coding agents -> Autonomous Software Factory.
  • Terminology:
    • Autocomplete
    • Coding Agents
    • Software Factory
  • Why it matters: Shows the historical context and necessity of evolving beyond isolated coding assistants.

Questions Everyone is Asking Right Now

  • Explanation: Addressing the major concerns enterprise engineering leaders have regarding AI software development.
  • Key claims:
    • Cost overruns with LLM APIs are a primary concern.
    • Locking into a single model provider restricts flexibility.
    • Team boundaries and organizational structures are blurring as product and engineering merge.
  • Examples:
    • Uber's CTO blowing through AI budgets or Microsoft clawing back cloud code licenses.
  • Terminology:
    • Cost overruns
    • Model agnosticism
    • SDLC integration
  • Why it matters: Identifies the exact enterprise friction points that prevent successful AI adoption.

Coding Agents Solved the Easy Part

  • Explanation: Arguing that actual code writing was never the main bottleneck in engineering; the surrounding process is.
  • Key claims:
    • Engineers spend less than 20% of their time writing code.
    • Code review, testing, debugging, planning, documentation, and meetings consume the rest.
    • Codebases go stale and docs fall out of sync rapidly.
  • Examples:
    • Pull request review times, test suite maintenance, and cross-team communication overhead.
  • Terminology:
    • Code review
    • Documentation
    • Codebase bottleneck
  • Why it matters: Shifts focus from raw code generation speed to holistic engineering process efficiency.

Signal to Deployment Workflow

  • Explanation: Defining the pipeline of a Software Factory from initial input signal to production monitoring.
  • Key claims:
    • A software factory is an integrated system of agents where every stage feeds another and agents share context.
    • Agents should handle triage, planning, implementation, evaluation, validation, shipping, and monitoring.
    • Teams should start narrow with verifiable workflows like incident triage before scaling up.
  • Examples:
    • Reading a Sentry alert, pulling traces, posting to Slack, and letting engineers review diagnoses.
  • Terminology:
    • Signal
    • Triage
    • Plan
    • Execute
    • Evaluate
    • Validate
    • Ship
    • Monitor
  • Why it matters: Provides a tangible architecture for building multi-agent software engineering systems.

Key Principles for Your Software Factory

  • Explanation: Three foundational pillars: model agnostic, sovereign deployment, and SDLC integration.
  • Key claims:
    • Factories must be model agnostic to optimize cost and performance across different tasks.
    • Deployment models must be sovereign to respect security and policy requirements.
    • Software factories must be integrated across the entire SDLC with no silos.
  • Examples:
    • Using GPT-5.2 for complex tasks or open-source models for code review at half the price.
  • Terminology:
    • Model Agnostic
    • Sovereign Deployment
    • SDLC Integration
    • Air-Gapped
  • Why it matters: Establishes enterprise-grade architectural requirements for AI agent deployment.

Agent Readiness and the Codebase as Bottleneck

  • Explanation: Evaluating codebase health across eight pillars to ensure AI agents can operate effectively.
  • Key claims:
    • If your agents and human engineers walk the same roads, paving them well is doubly useful.
    • Agent readiness is evaluated across validation, build systems, feedback loops, docs, and security posture.
    • You cannot govern what you cannot see.
  • Examples:
    • Linter checks, formatter rules, readmes, agent.md files, and unit test coverage.
  • Terminology:
    • Agent Readiness
    • Code Clarity
    • Test Reliability
    • Security Posture
  • Why it matters: Highlights that messy codebases block AI agent success just as much as human success.

Measuring Outcomes, Not Tokens

  • Explanation: Rejecting token-based leaderboards in favor of tangible engineering throughput and quality metrics.
  • Key claims:
    • Token leaderboards encourage profligate spending and incentivize agents to generate unnecessary tokens.
    • Measure outcomes like cycle time, autonomy ratio, code shelf life, incident MTTR, and cost per PR.
    • End users care about dramatic improvements in software delivery, not token spend.
  • Examples:
    • Signal to production time, human intervention count, and cost per pull request.
  • Terminology:
    • Outcome Measurement
    • Cycle Time
    • Autonomy Ratio
    • Incident MTTR
  • Why it matters: Aligns AI engineering metrics with actual business value rather than vanity metrics.

Key Points

Engineers spend under 20% of their time writing code

  • Explanation: The majority of engineering effort goes into planning, review, testing, debugging, and documentation.
  • Evidence: Mention of 80% of engineering time being spent on non-coding tasks.
  • Practical implication: AI engineering solutions must target the entire SDLC rather than just autocomplete code generation.

Model agnosticism prevents provider lock-in and cost explosion

  • Explanation: Organizations should route tasks to the most cost-effective and capable model dynamically rather than relying on a single provider.
  • Evidence: Open-source models can achieve similar performance to frontier models at a fraction of the cost.
  • Practical implication: Build agent systems that support multiple LLM backends and routing strategies.

Token metrics are gamible and misleading

  • Explanation: Measuring AI success by tokens generated encourages wasteful usage and bad engineering habits.
  • Evidence: Token leaderboards encourage profligate spending.
  • Practical implication: Focus measurement on real outcomes like cycle time, MTTR, and cost per PR.

Frameworks, Models & Processes

Software Factory Pipeline

  • How it works: An integrated system of agents taking an input signal and driving it through planning, execution, validation, and deployment.
  • Components:
    • Signal
    • Triage
    • Plan
    • Execute
    • Evaluate
    • Validate
    • Ship
    • Monitor
  • When to use: When scaling AI-driven software development across enterprise engineering teams.

Agent Readiness 8-Pillar Framework

  • How it works: Evaluates codebase health across multiple criteria to determine if it is ready for autonomous agent operations.
  • Components:
    • Code Clarity
    • Test Reliability
    • Modularity
    • Documentation
    • Security Posture
    • Validation
    • Feedback Loops
    • Observability
  • When to use: Before deploying autonomous coding or triage agents into production codebases.

Examples & Case Studies

Transitioning from coding autocomplete to agentic PR generation and incident triage.

  • Illustrates: Moving up the abstraction ladder of AI in software engineering.
  • Lesson: Engineers must evolve from direct coders to system stewards and governors.

Actionable Takeaways

  • Immediate:
    • Audit your codebase for agent readiness across validation, docs, and test suites.
    • Shift metrics away from token counts toward cycle time and autonomy ratio.
  • Strategic:
    • Adopt a model-agnostic and sovereign deployment architecture.
    • Integrate AI agents across the entire SDLC rather than isolated silos.
  • Questions to investigate:
    • How do we prevent cost overruns as models improve?
    • How do we ensure access to the best AI models regardless of who wins?
    • What happens to software engineers as agents take over execution?

Claims Worth Verifying

  • Engineers spend less than 20% of their time writing code (Statistic)
  • Open-source models can achieve code review performance comparable to GPT-5.2 at half the price (Comparative Performance Claim)

Notable Quotes

"Signal in, Software out. Humans govern." "If your kid needs a algebra tutor, you can definitely find someone cheaper than Albert Einstein." "No trust without control." (at 24:04) "Measure outcomes, not tokens." (at 27:04)

Compressed Summary

  • AI engineering has evolved from autocomplete to coding agents and full software factories.
  • Coding is only 20% of engineering; the rest is review, testing, docs, and planning.
  • Software factories must be model agnostic, sovereign, and integrated across the SDLC.
  • Measure success using outcome metrics like cycle time and autonomy ratio, not token usage.
  • Keywords: software factory, ai agents, sdlc, agent readiness, governance
  • Core insight: AI engineering is transitioning from coding assistants to autonomous software factories where human engineers act as stewards and governors of multi-agent systems.

Core insights

7
Mental Modelmedium noveltymoderate evidence

The real engineering bottleneck is the non-coding process, not code generation: engineers spend under 20% of their time writing code, so coding agents only attacked the easy, small fraction of the SDLC.

Why it matters

Reframes where to invest agent effort and budget: automating code generation captures a minority of cycle time, while review, testing, debugging, planning, docs, and coordination hold the majority of latency and cost.

Generalization

When automating a workflow, measure where time and cost actually concentrate before optimizing the most visible step; the most automatable step is often not the dominant cost.

Engineers spend less than 20% of their time writing code.
Open source video
AI engineering solutions must target the entire SDLC rather than just autocomplete code generation.
Open source video
Architecturemedium noveltymoderate evidence

A Software Factory is defined not by autonomous coding but by an integrated set of agents where every SDLC stage feeds the next and agents share context, spanning triage, plan, execute, evaluate, validate, ship, and monitor.

Why it matters

Shared cross-stage context is the architectural differentiator; isolated agents that each re-derive context lose the handoffs that carry most engineering value, and it forces design of a shared state/handoff layer rather than standalone agents.

Generalization

Multi-stage agent systems need a persistent shared context substrate across stages; per-agent stateless designs break down at handoffs.

A software factory is an integrated system of agents where every stage feeds another and agents share context.
Open source video
Agents should handle triage, planning, implementation, evaluation, validation, shipping, and monitoring.
Open source video
Practicemedium noveltymoderate evidence

Adoption should start with narrow, verifiable workflows (e.g., incident triage) rather than broad autonomy, because verifiability is what makes an agent workflow safe to expand.

Why it matters

Verifiability is a gating property for scaling autonomy; choosing a first workflow with an objective ground truth (a Sentry alert with traces and a confirmable diagnosis) constrains blast radius and enables evaluation before widening the scope.

Generalization

Sequence agent autonomy by verifiability of the outcome, not by business value or visibility.

Teams should start narrow with verifiable workflows like incident triage before scaling up.
Open source video
Reading a Sentry alert, pulling traces, posting to Slack, and letting engineers review diagnoses.
Open source video
Mechanismmedium noveltymoderate evidence

Model agnosticism means per-task dynamic routing, not just multiple providers: send complex tasks to frontier models and route cheaper work (e.g., code review) to open-source models at roughly half the price.

Why it matters

Cost and quality optimization are coupled to a routing layer that classifies tasks and dispatches to the best cost/performance backend, which is an explicit architectural component rather than a procurement preference.

Generalization

Any production agent system should treat model selection as a runtime routing decision keyed on task difficulty and cost sensitivity.

Factories must be model agnostic to optimize cost and performance across different tasks.
Open source video
Using GPT-5.2 for complex tasks or open-source models for code review at half the price.
Open source video
Practicemedium noveltymoderate evidence

Agent readiness is a codebase property measured across eight pillars (validation, build systems, feedback loops, docs, security posture), and investment in it is dual-use: smoothing the path for agents equally benefits human engineers.

Why it matters

Codebase hygiene becomes a prerequisite rather than a side task; linters, formatter rules, readmes, agent.md files, and test coverage are instrumentation that both agents and humans depend on.

Generalization

Agent-investable infrastructure should be evaluated by whether it also serves existing human workflows, to avoid parallel maintenance of agent-only scaffolding.

If your agents and human engineers walk the same roads, paving them well is doubly useful.
Open source video
Agent readiness is evaluated across validation, build systems, feedback loops, docs, and security posture.
Open source video
Failure Modehigh noveltymoderate evidence

Token-based measurement is structurally gamable: token leaderboards reward profligate spending and incentivize agents to produce unnecessary tokens, so measurement must shift to outcome metrics like cycle time, autonomy ratio, code shelf life, incident MTTR, and cost per PR.

Why it matters

Choosing the wrong optimization target actively degrades the system, since agents optimize whatever is measured; outcome metrics align agent behavior with delivery throughput and quality while preventing cost blowouts.

Generalization

Never use a proxy that scales with effort as the success metric for an optimizing agent; use downstream outcome measures the agent cannot inflate trivially.

Token leaderboards encourage profligate spending and incentivize agents to generate unnecessary tokens.
Open source video
Measure outcomes like cycle time, autonomy ratio, code shelf life, incident MTTR, and cost per PR.
Open source video
Architecturemedium noveltyweak evidence

Sovereign deployment (including air-gapped operation) is framed as a co-equal architectural pillar alongside model agnosticism and SDLC integration, meaning agent factories must satisfy security and policy constraints without abandoning model flexibility.

Why it matters

Security posture and governance are positioned as design constraints that shape deployment topology, not post-hoc compliance add-ons; it also pairs with the claim that you cannot govern what you cannot see.

Generalization

Enterprise agent platforms must decouple the orchestration/control plane from model backends so deployment constraints can vary per environment.

Deployment models must be sovereign to respect security and policy requirements.
Open source video
You cannot govern what you cannot see.
Open source video

Deep dives

4

Agent Readiness Scoring and Gating

Research question

What are the concrete eight agent-readiness pillars and how do they correlate with agent task success, and can they be scored into a usable gate for autonomy?

Why

Agent readiness is a prerequisite for safe autonomy expansion, but without a concrete rubric and correlation data, teams cannot know when a codebase is ready for agents.

Agent readiness is evaluated across validation, build systems, feedback loops, docs, and security posture.
Open source video
If your agents and human engineers walk the same roads, paving them well is doubly useful.
Open source video
Source video

Cross-Stage Shared Context in Agent Factories

Research question

How much cross-stage shared context is needed before the factory outperforms isolated agents, and what is the cost of carrying it?

Why

Shared context is the architectural differentiator of a Software Factory, but the optimal granularity and cost trade-off are unknown.

A software factory is an integrated system of agents where every stage feeds another and agents share context.
Open source video
Source video

Quality-Preserving Model Routing for Code Review

Research question

Does dynamic model routing to cheaper open-source models preserve quality on code review tasks compared to frontier models at matched cost?

Why

Model agnosticism is proposed for cost optimization, but without quality benchmarks, routing to cheaper models risks defect escapes.

Using GPT-5.2 for complex tasks or open-source models for code review at half the price.
Open source video
Factories must be model agnostic to optimize cost and performance across different tasks.
Open source video
Source video

Outcome Metrics vs Token Metrics for Agent Productivity

Research question

Do agents optimizing outcome metrics (cycle time, cost per PR) outperform and produce less waste than agents optimizing token-volume metrics?

Why

Token metrics are gamable and lead to cost blowouts; outcome metrics are proposed but their behavioral impact needs validation.

Token leaderboards encourage profligate spending and incentivize agents to generate unnecessary tokens.
Open source video
Measure outcomes like cycle time, autonomy ratio, code shelf life, incident MTTR, and cost per PR.
Open source video
Source video

Article ideas

4

The Software Factory Is Not About Coding Agents: Why the 80% of Engineering Work Is the Real Target

Automating code generation addresses only a small fraction of engineering cycle time; the real value of a Software Factory comes from integrating agents across the full SDLC—triage, planning, evaluation, validation, shipping, and monitoring—where most latency and cost reside.

Angle

Reframe the AI engineering conversation from coding assistants to process-wide automation, using time-allocation data and the factory pipeline.

Source video

Token Leaderboards Are a Productivity Trap: Switch to Outcome Metrics for AI Engineers

Measuring agent productivity by token volume incentivizes wasteful generation and cost overruns; teams must adopt outcome metrics like cycle time, autonomy ratio, code shelf life, incident MTTR, and cost per PR to align agents with delivery value.

Angle

Expose the perverse incentives of token-based KPIs and provide a migration path to outcome instrumentation.

Source video

Model Agnosticism Is a Routing Problem, Not a Procurement Policy

Treating model choice as a runtime routing decision—dispatching complex tasks to frontier models and cheaper open-source models for routine work like code review—optimizes cost and performance, but requires new routing and evaluation infrastructure.

Angle

Contrast static provider lock-in with dynamic per-task routing, and outline the engineering needed to validate routing quality.

Source video

Pave the Roads for Agents and Humans Alike: The Dual-Use Case for Agent Readiness

Investing in agent readiness—validation, build systems, feedback loops, docs, security posture—benefits human engineers and agents equally, making codebase hygiene a strategic prerequisite rather than a side task.

Angle

Argue that agent readiness is not agent-only scaffolding but shared infrastructure, with examples like linters, readmes, and agent.md files.

Source video

Project ideas

4

Agent Readiness Scorer

gatehouse

Repositories with higher agent-readiness scores (across validation, build systems, feedback loops, docs, security posture) will have measurably higher agent task success rates (e.g., PR merge rate) and lower human intervention.

Proof of concept

Implement a static analysis tool that scores a repo on the eight pillars; run the same coding agent on a set of tasks across repos with varied scores; measure success and intervention.

Measurement

Correlation between readiness score and agent success rate; regression of success on individual pillars.

Source video

Shared Context Ablation for Software Factories

movement-lab

Adding shared cross-stage context to a pipeline of SDLC agents reduces cycle time and increases autonomy ratio compared to isolated per-stage agents, but with diminishing returns beyond a certain context scope.

Proof of concept

Build two minimal pipelines: one with shared context store across triage, plan, implement, test, ship; one with isolated agents that re-derive context. Run on synthetic bug reports and measure.

Measurement

Cycle time (signal to production), autonomy ratio (human interventions per task), cost per PR.

Source video

Code Review Model Routing Benchmark

beyond-evals

At matched defect-escape rate, open-source models can perform code review at roughly half the cost of frontier models.

Proof of concept

Create a dataset of code review tasks with known defect labels; run GPT-5.2 and a leading open-source model on the same tasks; measure review acceptance, defect escape, and cost.

Measurement

Cost per review, defect escape rate, F1 of defect detection, at matched budgets.

Source video

Outcome-Optimized Agent Harness

new

Agents configured with outcome objectives (e.g., minimize cycle time or cost per PR) will achieve better delivery metrics and lower token waste than agents optimized for token volume.

Proof of concept

Implement two agent harnesses: one rewards token generation, one rewards outcome metrics; run on a backlog of tasks; compare.

Measurement

Cycle time, cost per PR, token usage, quality (defect escape).

Source video

Architectural implications

4

Agents must carry context across SDLC stages rather than re-deriving it per stage.

Before

Isolated coding agents operating on single tasks with no shared state.

After

An integrated factory where every stage feeds another and agents share a common context.

Consequence

Requires an explicit shared-context/state layer and defined handoff contracts between stages, otherwise the pipeline degrades at boundaries.

Source video

Model choice becomes a runtime routing decision.

Before

A single model provider fixed at integration time.

After

A routing layer dispatching tasks to frontier or cheaper open-source models based on task complexity and cost.

Consequence

Adds task-classification and routing infrastructure as a first-class component, and creates a new evaluation surface: routing correctness.

Source video

Codebase hygiene is promoted from human convenience to agent prerequisite.

Before

Codebase quality treated as a maintainability concern for human engineers.

After

Agent readiness measured across eight pillars (validation, builds, feedback loops, docs, security posture) as a gating condition for agent autonomy.

Consequence

Agent rollouts become gated on measurable codebase readiness, and readiness tooling earns investment because it serves both agents and humans.

Source video

Success measurement shifts from consumption proxy to delivery outcome.

Before

Token leaderboards and token-spend-based evaluation of agent productivity.

After

Outcome metrics: cycle time, autonomy ratio, code shelf life, incident MTTR, cost per PR.

Consequence

Evaluation harnesses must instrument the SDLC pipeline end to end (signal-to-production time, human intervention count) rather than the model call.

Source video

Tradeoffs and failure modes

5

Model agnosticism and routing

Benefit

Optimizes cost and performance per task; avoids provider lock-in and cost explosion.

Cost or risk

Adds routing/classification complexity and a multi-backend evaluation burden; each backend must be independently validated for quality.

Factories must be model agnostic to optimize cost and performance across different tasks.
Open source video
Source video

Scaling autonomy breadth

Benefit

Broader coverage of the SDLC and larger end-to-end automation.

Cost or risk

Wider scope on unverifiable workflows multiplies blast radius; the recommendation is to start with verifiable workflows like incident triage.

Teams should start narrow with verifiable workflows like incident triage before scaling up.
Open source video
Source video

Token-volume metrics as productivity proxy

Benefit

Trivially easy to instrument and compare across agents and teams.

Cost or risk

Gamable: incentivizes agents to generate unnecessary tokens and encourages profligate spend without delivering delivery outcomes.

Token leaderboards encourage profligate spending and incentivize agents to generate unnecessary tokens.
Open source video
Source video

Sovereign deployment constraints

Benefit

Respects security and policy requirements, enabling air-gapped and regulated environments.

Cost or risk

Constrains available model backends and may exclude frontier models, conflicting with per-task routing for cost/performance.

Deployment models must be sovereign to respect security and policy requirements.
Open source video
Source video

Documentation and codebase freshness under agent speed

Benefit

Agents can operate on and be guided by docs (e.g., agent.md files, readmes).

Cost or risk

Codebases go stale and docs fall out of sync rapidly, degrading the shared context agents rely on.

Codebases go stale and docs fall out of sync rapidly.
Open source video
Source video

Open questions

5

What are the concrete eight agent-readiness pillars and how are they scored into a usable gate for autonomy?

Why unresolved

The summary names categories (validation, build systems, feedback loops, docs, security posture) but not the full set or the scoring method.

Research direction

Build an agent-readiness rubric and test whether score correlates with agent task success rate on a real repo.

Source video

How much cross-stage shared context is needed before the factory outperforms isolated agents, and what is the cost of carrying it?

Why unresolved

The summary asserts stages feed each other and share context but gives no mechanism, granularity, or cost data.

Research direction

Ablate shared-context scope (per-stage vs full-pipeline memory) and measure cycle time, autonomy ratio, and cost per PR.

Source video

Does dynamic model routing actually preserve quality on routed-to-cheaper tasks like code review?

Why unresolved

The 'half the price' claim is asserted without quality comparison data.

Research direction

Benchmark open-source vs frontier models specifically on code review acceptance/defect-escape rates at matched cost budgets.

Source video

What autonomy ratio and human-intervention thresholds are safe to target per workflow class?

Why unresolved

Outcome metrics are named but no target values or thresholds are given.

Research direction

Longitudinal measurement of signal-to-production time and human intervention count per workflow, with incident MTTR as a safety counter-metric.

Source video

How do sovereign/air-gapped deployment constraints interact with model-agnostic routing?

Why unresolved

Both are stated as pillars but no reconciliation of backend availability under security policy is provided.

Research direction

Enumerate deployment topologies (cloud, VPC, air-gapped) against routing strategy and quantify the cost/quality delta from excluded backends.

Source video

Key claims

8
factualVerification needed

Engineers spend less than 20% of their time writing code.

Evidence

Engineers spend less than 20% of their time writing code.

Question

What does time-allocation telemetry across engineering orgs show for the coding share, and how does it vary by team and seniority?

Source video
causalVerification needed

A software factory is an integrated system of agents where every stage feeds another and agents share context.

Evidence

A software factory is an integrated system of agents where every stage feeds another and agents share context.

Question

Does shared cross-stage context measurably improve end-to-end outcomes versus isolated per-stage agents?

Source video
opinionVerification not requested

Teams should start narrow with verifiable workflows like incident triage before scaling up.

Evidence

Teams should start narrow with verifiable workflows like incident triage before scaling up.

Source video
comparativeVerification needed

Open-source models can perform code review at roughly half the cost of frontier models.

Evidence

Using GPT-5.2 for complex tasks or open-source models for code review at half the price.

Question

At matched defect-escape and review-acceptance quality, what is the actual cost delta between open-source and frontier models on code review?

Source video
causalVerification needed

Token leaderboards encourage profligate spending and incentivize agents to generate unnecessary tokens.

Evidence

Token leaderboards encourage profligate spending and incentivize agents to generate unnecessary tokens.

Question

Do agents with token-volume objectives measurably inflate output tokens without improving task outcomes?

Source video
opinionVerification not requested

If agents and human engineers walk the same roads, paving them well is doubly useful.

Evidence

If your agents and human engineers walk the same roads, paving them well is doubly useful.

Source video
factualVerification not requested

You cannot govern what you cannot see.

Evidence

You cannot govern what you cannot see.

Source video
predictionVerification needed

Engineers are moving from typing code to governing agents.

Evidence

Engineers are moving from typing code to governing agents.

Question

Does the observed ratio of engineering time shift from authoring to review/governance activity as agent autonomy increases?

Source video

Connections

5