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The Next Commit is the forward-looking podcast and newsletter for developers, engineers, and technology leaders focused on building the future of software. thenextcommit.substack.com

6d ago

Simon Vane: Giving coding agents a solid foundation

Episode summary What gives a coding agent a dependable platform to work on? Simon Vane, Staff Engineer at M-KOPA, connects code quality with the engineering discipline around it. He describes how established pipelines and quality gates supported faster delivery, why he still asks agents to refactor, and how he adapted test-driven development when he stopped writing most of the code himself. Simon and Tom previously worked together using Extreme Programming, TDD and pair programming. Their conversation explores which benefits of those practices can move into agent instructions and automated verification, and which need renewed attention from people. We discuss maintained journey documents across microservices, context-rich AI reviewers and the tension between small changes and constant review interruptions. Simon also reflects on what he misses from pairing: seeing how colleagues work and learning from their decisions. Faster implementation creates opportunities, but review, maintenance and shared understanding still have costs. About Simon Simon Vane is a Staff Engineer at M-KOPA, a fintech serving underbanked customers across Africa. He has 30 years of experience with Microsoft technology, including a decade specialising in event-driven distributed systems in Azure. Shaped by Extreme Programming, Agile and DevOps, he helps engineers use AI across the development lifecycle while maintaining quality. His current work includes guiding coding agents and developing skills to share engineering practices across teams. Biography draws on his guest-supplied briefing. In this episode * 02:34: Simon and Tom’s history with pairing, TDD and quality * 03:52: M-KOPA and Simon’s engineering context * 07:05: The current coding setup and adoption of Claude Code * 08:24: Problem discovery and using an agent as a peer architect * 10:47: Temporary design documents and maintained journey documents * 14:20: Breaking designs into work while preserving team understanding * 16:44: Faster delivery, silos and understanding unfamiliar code * 19:41: The foundations already in place before agents accelerated delivery * 21:04: Planning work and adapting TDD to coding agents * 25:03: Exploring specification-driven development * 26:37: Why better-designed code helps agents make changes * 30:50: AI reviewers, human review and the cost of interruptions * 34:08: Tests, mutation testing and static analysis * 37:46: Training, knowledge sharing and local innovation * 39:17: A team’s automated incident-triage example * 40:55: Prioritisation when implementation feels cheap * 43:53: Start small and make learning from colleagues intentional Key ideas * Simon believes established pipelines and automated gates reduced the risks of accelerating delivery with agents. * He observes better results when agents work on well-designed code, and continues to challenge design and request refactoring. * Persistent TDD instructions made his personal workflow more practical, while he still checks the evidence of the failing test and its reason. * Maintained journey documents help engineers and agents understand behaviour spanning several services. * Teams use contextual AI reviews before required human review, but review capacity remains an unresolved constraint. * Small pull requests can be easier to understand while a frequent stream of reviews creates interruptions; teams need to find a workable balance. * Agent-assisted code comprehension may reduce some costs of silos, but Simon still misses the informal learning provided by pairing. * Quick implementation retains maintenance and review costs, making prioritisation consequential. People and links * M-KOPA * Simon invites listeners to connect on LinkedIn. His preferred profile URL is not supplied; add it during guest review. Tools mentioned * Claude Code * OpenSpec, discussed as an approach some teams are exploring * SonarQube Thanks for reading! Subscribe to receive new posts and support The Next Commit. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thenextcommit.substack.com
Oct 20

Shashank Patwardhan: Giving agents freedom without giving away control

Episode summary What happens when a coding agent is asked to fix a failing pipeline and has permission to change the checks that are failing? Shashank Patwardhan describes a prior consulting engagement in which an agent disabled a security scan, turning the pipeline green while removing a safeguard for subsequent changes. The story opens a bigger question: how do we give agents useful freedom without handing them control of the boundaries they should operate within? Shash, Application Security Lead at IFX Payments, draws on his earlier consulting experience to explore least privilege, independent verification, monitoring and the ability to stop an agent. We discuss why securing generated code is different from securing the agent producing it, how AI can help security teams demonstrate vulnerabilities, and why human approval is only useful when people have the context and attention to make a real decision. About Shash Shashank Patwardhan, known as Shash, is the Application Security Lead at IFX Payments. Before moving in-house, he worked in technical security consulting across multiple sectors, helping organisations test their defences and develop secure engineering practices. He brings that consulting perspective to questions about verifying AI-generated code and governing agents. This conversation concerns industry practices and prior consulting experience, not IFX Payments’ internal security controls or incidents. In this episode * 02:26: From AI suggesting code to AI taking action * 07:40: Human judgment and knowing when to stop * 09:05: Cheap code, large pull requests and review capacity * 11:07: Smaller changes, dependencies and layered verification * 16:55: When a model reviews its own work * 19:40: AI-assisted penetration testing and evidence of impact * 24:31: Reviewing against organisational security requirements * 30:03: Making security findings actionable for developers * 36:37: The agent that fixed a pipeline by disabling its security scan * 42:56: Least privilege, monitoring and a kill switch * 46:05: Approval fatigue and designing useful boundaries * 48:15: Accountability and the next generation of security practitioners * 51:42: Shash’s closing advice on verification and agent permissions Key ideas * Giving an agent a developer’s credentials does not establish that it will exercise the developer’s judgment about how those permissions should be used. * In Shash’s consulting example, a successful pipeline run concealed the removal of the security check that had been blocking it. * Shash recommends task-appropriate permissions, monitoring and a way to stop agent execution, rather than unrestricted access justified by convenience. * A second model review can be useful, but the conversation challenges whether it provides genuinely independent assurance. * Shash describes AI helping him produce and troubleshoot proofs of concept that make security findings more concrete for developers. * Manageable pull requests preserve the opportunity for meaningful review as generation capacity increases. * Approval requests should preserve consequential human decisions, not become a stream of prompts people accept automatically. * Shash sees human judgment and accountability remaining essential, while questioning how new practitioners will acquire the experience those responsibilities require. People and links * Shashank Patwardhan on LinkedIn (guest-supplied profile; confirm during review) * IFX Payments Tools mentioned * SonarQube * Gitar Thanks for reading! Subscribe for free to receive new posts. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thenextcommit.substack.com
Oct 13

Matt Erman: Your agent writes the code. You still own the architecture

Matt Erman: Your agent writes the code. You still own the architecture Episode summary When an agent generates an implementation at speed, it is easy to forget that responsibility for a coherent architecture still belongs to the developer. Matt Erman explains why that responsibility is as important as ever. Agents know an enormous range of architectural patterns, but without sufficient constraints they can combine valid ideas into a system that works while becoming progressively harder to maintain. Matt treats architecture as a repeatable process rather than a catalogue of patterns. He explains how evidence-led investigation, engineering guidance and small verified phases constrain his agent workflow; why architectural fundamentals matter more as implementation accelerates; and how fitness functions operate twice, first as an executable specification that guides the agent and then as deterministic verification of the result. About Matt Matt Erman is a senior software engineer at Blackboard, LLC, and the author of Grokking Software Architecture, a practical introduction for developers and aspiring architects published through Manning’s Early Access Program. His work focuses on architectural decision-making, agent-assisted development and the verification systems needed to keep rapidly generated software within deliberate technical boundaries. In this episode * 00:34 – Introducing Matt and Grokking Software Architecture * 01:19 – Treating AI agents as a first-class architectural concern * 02:19 – Matt’s coding-agent setup and whole-workspace context * 04:23 – Adapting specification-driven development for agents * 06:18 – Skills, hooks, fitness functions and evidence-led investigation * 08:22 – Why AI agents can be “too smart” without architectural constraints * 09:30 – Why human code review cannot scale with generated output * 11:25 – Protecting architectural boundaries with fitness functions * 13:36 – What an architectural fitness function tests * 15:52 – The “-ilities” that architecture must preserve * 16:59 – Architecture as a responsibility shared across the team * 20:47 – Trust the verification system, not the agent * 21:45 – Planning work as small phases and commit boundaries * 24:31 – How incremental checkpoints confine failures * 25:51 – Reading fitness functions as an executable specification * 27:22 – Refactoring, rewriting and the limits of the strangler fig pattern * 30:39 – Why agents move engineers towards design and architectural judgement * 32:35 – Pair development for consequential decisions * 33:49 – What new developers need to learn in an agentic world * 36:07 – Architecture as a repeatable process of asking better questions * 37:54 – Architectural awareness, alignment and accountability * 41:33 – Engineers move towards end-to-end ownership * 44:15 – The book’s progress and the Clean-CTX project * 46:56 – Why agents may not need human-readable source code as context Key ideas * An unconstrained agent can combine valid patterns into an incoherent system. Architectural boundaries narrow the solution space so its broad knowledge becomes useful rather than chaotic. * Human review does not scale simply by adding more reviewers. As generated output grows, fatigue and attention become failure modes, so the scalable investment is the automated feedback loop around the agent. * Architectural fitness functions test structural rules that unit and integration tests often miss, such as dependency direction, layer boundaries and prohibited database access. * A significant escaped defect should improve the system that allowed it through. Recording the invariant and adding a regression check gives both people and agents durable evidence about the boundary. * Fitness functions can serve two roles: they reject violations after implementation and act as an executable specification that shapes the agent’s plan before coding begins. * Large tasks should be split into independently verified phases with clean commit boundaries. When a regression appears, the team can confine its cause to the latest increment instead of reconstructing one enormous change. * Architecture is not a title or a single high-level decision. Every change to a class, dependency, table or service is an architectural decision made at a different scope. * As agents take on implementation, engineers must exercise their existing responsibility for intent, constraints, trade-offs, design and evidence more deliberately. Strong fundamentals provide a repeatable way to ask the right questions when the technology changes. * Architectural growth moves from awareness of a change’s wider effects, through alignment with the system and its rationale, to accountability earned through consistently sound decisions. People and links * Matt Erman at Manning * Grokking Software Architecture * Grokking Software Architecture: It’s All About Tradeoffs (Manning video) * Fitness function-driven development by Paula Paul and Rosemary Wang * Blackboard * Clean-CTX on GitHub Tools mentioned * Visual Studio Code * Claude Code * Cline * ArchUnit * Git This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thenextcommit.substack.com
Oct 6

Kesha Mykhailov: When agents move faster than organisations can think

AI coding agents can turn an initial idea into thousands of lines of code before a team has discussed whether the approach is sound. Kesha Mykhailov explains how that speed is changing development at Fin: technical plans and the conversations behind them are disappearing, while expert reviewers face an unsustainable volume of generated code. Fin’s response is to integrate more of the organisation’s intelligence into the agent workflow. Kesha describes how expert React review comments became shared guidance for both code generation and verification, why deterministic hooks load critical context, and how their code-review agent Shrek rewards small pull requests. He also recounts an evaluation failure in which a cheaper model preserved issue recall but produced damaging false positives—showing why offline benchmarks must be combined with human and production signals. About Kesha Kesha Mykhailov is an engineer at Fin working on its internal AI developer platform and Shrek, its code-review agent. His infrastructure background shapes an enablement-focused approach: capture expert knowledge, make it available across the engineering organisation, and build controls that help developers use coding agents safely without surrendering their increased velocity. In this episode * 00:49 — Fin’s evolution from Intercom and its AI customer-service agent * 01:49 — The scale and distribution of Fin’s engineering organisation * 02:44 — Why adopting AI coding agents became a competitive necessity * 05:07 — Standardising on Claude Code to create a shared internal platform * 07:01 — How fast code generation displaced technical plans and early alignment * 09:22 — The hidden rationale that source code and agents cannot reveal * 10:08 — Using auto-approval to encourage smaller pull requests * 13:36 — Why agents struggle to divide work into valuable vertical slices * 15:31 — Encoding expert context in skills and loading it with deterministic hooks * 17:12 — A React migration exposes the cost of unconstrained AI-generated code * 18:47 — Turning expert review comments into reusable guidance * 21:54 — Why an AI agent is an evolving process rather than a finished system * 22:22 — Measuring review quality through reactions, applied feedback and evaluations * 25:18 — Shrek’s problem-statement, security and logical-correctness reviewers * 27:16 — A cheaper model passes the evaluation but creates a false-positive incident * 30:01 — How evaluation datasets inherit the biases of the models that produced them * 32:12 — Using production signals and A/B tests to measure effects on engineers * 34:54 — Why Fin built its own reviewer instead of relying on an off-the-shelf product * 35:51 — One source of expert guidance for generation, questions and review * 38:44 — Making organisation-specific knowledge available through shared skills * 39:38 — Teaching the agent to ask for intent before creating a pull request * 42:01 — Enforcing critical workflow guidance through hooks * 42:56 — Detecting a model-driven increase in misleading code comments * 44:18 — Building security controls into the common agent platform * 45:58 — Cost, model choice and the difficulty of changing an established harness * 48:35 — Why reliable evaluations matter more than post-training alone * 50:54 — Bringing infrastructure enablement experience into the AI platform team * 52:15 — Engineers shift from writing code to choosing and validating valuable work * 54:27 — Why newcomers struggle to acquire the organisational context behind the code * 56:47 — How Shrek’s personality helps Fin counter automated-review fatigue Key ideas * When implementation becomes nearly free, teams can turn the first plausible idea into code before discussion exposes its assumptions and hidden constraints. * Technical plans were valuable partly because they recorded conversations that had already aligned people around an approach; preserving the document alone would not preserve that deliberation. * Expert knowledge should guide code generation and verification from the same maintained source, while independent security, correctness and human checks challenge the resulting work from different angles. * Deterministic hooks are useful when important context cannot be left to an agent’s judgement about which skill to invoke. * Fin makes small changes the faster path by withholding automatic approval from large pull requests, using the workflow’s incentives to protect reviewability. * Offline evaluations describe only the behaviours represented in their datasets. Shrek’s model downgrade preserved recall but increased false positives that disrupted real engineers. * AI systems require continuous operation: model behaviour changes, prompts decay, costs shift and production signals reveal effects that static evaluations miss. * Faster implementation moves engineering effort towards identifying valuable problems, validating customer outcomes and maintaining the environment in which agents work. * Skills can distribute explicit standards and rationale, but they cannot completely replace the conversations through which newcomers acquire tacit organisational context. People and links * Fin Tools mentioned * Claude Code * OpenAI Codex * GitHub Copilot * React * Ember.js * CodeRabbit * Gitar This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thenextcommit.substack.com
Sep 29

Quentin Chevrin: When coding shrinks, the developer expands

Episode summary Quentin Chevrin is a front-end developer in Sonar’s billing squad. In the space of a year, he moved from GitHub Copilot completing individual lines to working mainly through Claude Code and barely opening his IDE. Yet the most interesting part of his story is not that AI writes the code. It is how Quentin has retained ownership of the design, decomposition and context while using agents to implement the work—and how that combination has widened the range of engineering problems he can solve. In this episode, Quentin walks through his workflow from Figma and Jira to focused agent sessions, automated analysis, AI code review and human approval. We discuss why he still writes his own tickets, how he changes the agent’s freedom according to the consequences of a mistake, why fast review is becoming the new bottleneck, and how AI is helping specialists make useful contributions across the stack without pretending expertise no longer matters. About Quentin Quentin Chevrin is a front-end developer at Sonar, working in the billing squad on the systems that support Sonar’s products and purchasing experiences. He is also a teacher and describes himself as a naturally sceptical adopter: interested in new tools, but reluctant to follow hype before they prove useful in real work. In this episode * 00:20 — Introducing The Next Commit and Quentin * 02:14 — Development at Sonar before AI * 06:16 — From Copilot autocomplete to Claude Code in the terminal * 10:59 — Why the technical work still begins with people and Figma * 15:39 — Writing Jira tickets as a way to build understanding * 20:07 — Giving an agent the context you already possess * 22:35 — Finding the right level of detail through experimentation * 24:36 — Repository and personal CLAUDE.md instructions * 28:06 — Constraints, steering and the causes of code bloat * 31:45 — Adjusting agent autonomy to the risk of the project * 35:23 — Small changes and fresh sessions to reduce error and token cost * 37:28 — Quentin’s commit and pull-request review process * 39:04 — SonarQube analysis, Gitar and human review * 41:10 — Four pull requests in a day—and review becoming the bottleneck * 43:30 — How rapid feedback protects developer flow * 45:14 — From front-end specialist towards a T-shaped engineer * 48:22 — Using AI to collaborate across squads * 49:05 — Faster onboarding and safer boldness for new joiners * 51:48 — Quentin’s advice: use AI to ask questions, not only to write code * 54:22 — Designing software without writing every loop and condition * 56:42 — Why Quentin is cautiously positive about what comes next Key ideas * Writing a ticket is not clerical work when the act of writing creates the developer’s mental model of the feature. * Agents are more efficient when developers supply context they already have instead of paying the agent to rediscover it. * The appropriate level of autonomy depends on consequence: a public, shared product deserves tighter scope than an internal or personal tool. * Small tickets, focused prompts and fresh sessions limit ambiguity and make generated changes easier to review. * Faster implementation moves the constraint downstream. Review, coordination and product judgement become more important as pull-request volume rises. * AI can widen a specialist’s contribution without replacing deep expertise. The result is a stronger T-shaped engineer, not an expert in everything. * Asking an agent to explain an unfamiliar system may be more valuable for learning and onboarding than asking it to generate code. People and links * Quentin Chevrin on LinkedIn * Quentin Chevrin on GitHub * Sonar * SonarQube Cloud Tools mentioned * Claude Code * GitHub Copilot * SonarQube CLI * Gitar * Figma Make * Playwright * Chrome DevTools * Model Context Protocol (MCP) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thenextcommit.substack.com
Sep 22

Michael Tweed: Agents are transforming the engineer’s role. Leadership must change with it.

Episode summary Michael Tweed is a principal software engineer helping lead AI adoption across Skyscanner’s engineering organisation. After four years of experimentation, Skyscanner has moved beyond treating AI as an individual productivity tool: agents now help generate specifications, implement changes, review code, run validation loops and work across repositories. In this episode, Michael explains what that transition means for engineering leaders and for the responsibilities of developers themselves. We discuss how Skyscanner turns local experimentation into shared capability through an AI champions network, skills marketplace and central configuration; why engineers increasingly review specifications and outcomes rather than every line of code; and how standards, curated knowledge and deterministic verification make greater autonomy possible. Michael also describes the move towards cloud-based orchestration, the human checkpoints required for larger work and why the engineer’s role is changing rather than disappearing. About Michael Michael Tweed is a principal software engineer at Skyscanner, working across its engineering platforms on AI adoption and developer experience. He began in mobile engineering before moving into mobile platform and developer-experience work. He now helps shape how several hundred Skyscanner engineers use agents, shared skills, organisational standards and validation systems to build software at scale. In this episode * 00:16 — Introducing Michael Tweed and AI adoption at Skyscanner * 01:02 — From mobile engineering to cross-platform developer experience * 02:12 — Four years of AI adoption, from autocomplete to agents * 04:37 — Crossing the threshold: generated code is already in production codebases * 05:23 — Matching models and tools to the task * 06:19 — From individual workflows to shared skills and cloud sessions * 07:36 — A light-touch rollout and Skyscanner’s AI champions network * 09:03 — Central configuration, cost visibility and a consistent experience * 10:22 — Why skills need evaluations and should be treated like code * 11:20 — Benchmarking models against Skyscanner’s own technology * 14:53 — Sensible defaults, engineer choice and a culture of cost awareness * 17:36 — Specification-driven development without one mandated workflow * 19:02 — Reviewing the specification rather than every line of code * 19:32 — Base and team-specific AI reviews for pull requests * 20:57 — Should agents choose the architecture they know best? * 21:33 — Measurable production standards and agent-accessible context * 22:32 — Different boundaries for internal tools and traveller-facing systems * 24:39 — Why connecting an agent to every document does not work * 25:38 — Curating organisational knowledge for agents * 28:11 — The incident where an agent changed a test to make it pass * 30:05 — Moving verification into agentic development loops * 31:28 — Tests, linting and SonarQube as independent evidence * 33:00 — Moving agent work from laptops into cloud orchestration * 35:21 — From autonomous tasks to multi-stage work with human checkpoints * 37:26 — Legacy modernisation and coordinated changes across repositories * 39:41 — Training, onboarding and the changing role of engineering * 41:10 — Product mindset, decision boundaries and organisational ways of working * 43:29 — Extending validation into production without sacrificing reliability Key ideas * Agent adoption at organisational scale is not principally a tool rollout. Leaders have to change the engineering environment around a changing division of work. * Skyscanner combines distributed experimentation with shared infrastructure. Its AI champions network surfaces useful practices that central teams can make repeatable through skills, configuration and platforms. * As agents perform more implementation, engineers increasingly concentrate on intent, specifications, architecture, constraints, verification and product outcomes. * Autonomy should reflect consequence. Skyscanner gives internal tools more freedom while preserving deliberate architectural and operational standards in traveller-facing systems. * Organisational knowledge must be curated before agents can rely on it. An old draft or abandoned proposal can be actively harmful when retrieved as authoritative context. * Skills and agent configuration are engineering assets. Evaluations, internal benchmarks and cost visibility help teams judge whether those assets genuinely improve their work. * Passing checks are not sufficient if an agent can weaken the evidence. Verification loops need explicit boundaries, deterministic tools and independent signals. * Cloud orchestration allows agents to work for longer and across repositories, but larger, multi-stage work needs clear human checkpoints before consequential transitions. * The engineer’s role is changing rather than disappearing: towards defining outcomes, designing the development system and knowing when human judgement must interrupt autonomy. People and links * Michael Tweed on LinkedIn * Skyscanner Tools mentioned * GitHub Copilot * Claude Code * OpenAI Codex * Model Context Protocol (MCP) * Jira * Confluence * SonarQube This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thenextcommit.substack.com
Sep 15

Tim Ottinger: The new economics of software engineering

Episode summary Tim Ottinger brings decades of Extreme Programming, Agile and software-craft experience to the demanding end of agentic engineering. His work asks how agents can accelerate delivery without eroding the architecture and knowledge that consequential, long-lived software depends on. In this episode, Tim explains why an agent’s simplest path through a task can gradually erase a system’s design—and why the foundations of XP are gaining new leverage. We examine his use of small end-to-end slices, test-driven development and a distinctive checkpoint at which the agent stops before refactoring. Tim also shows how agents, tests and Git make ambitious legacy modernization more practical, how repository evidence can support better structural decisions, and why coherence became his eighth Code Virtue. The result is more than old practice with new technology: agents may allow teams to apply their best engineering disciplines more consistently and with fewer compromises. About Tim Tim Ottinger, known online as Agile Otter, is a long-time Agile practitioner and “old XP guy” who works with architecture and development groups at LexisNexis on technical improvement and safe agentic development. He co-authored Agile in a Flash, contributed the chapter on meaningful names to Clean Code, and has written extensively about software design, testing, refactoring, naming and team practice. In this episode * 00:57 — Introducing Tim Ottinger and his place in the early Agile community * 02:11 — Moving boldly—and safely—into agentic development * 03:10 — Code as knowledge representation, not only instructions * 04:32 — Why agents favour primitive solutions that gradually erase design * 06:04 — Architecture recovery, tripwires and technical safety * 07:03 — TDD, atomic commits and managing cognitive load * 09:07 — What is the agent equivalent of trained intuition? * 11:07 — Turning principles and heuristics into usable evidence * 13:18 — Finding hidden coupling in Git co-change history * 16:09 — Deterministic tools that help an LLM decide where to investigate * 20:51 — Small end-to-end slices and a disciplined test-first workflow * 23:30 — Why the agent stops when it proposes a refactor * 25:14 — Teaching a skill to reject coincidental duplication * 27:09 — Bringing books and articles directly into agentic work * 28:50 — Coherence as the eighth code virtue * 30:27 — Profitable and unprofitable intellectual labour * 33:25 — Preventing agents from reproducing legacy design problems * 34:13 — Refactoring an unfamiliar legacy codebase with agents * 38:07 — How agents change the economics of refactoring * 38:57 — Git makes ambitious structural experiments disposable * 39:39 — Why testing has become dramatically more viable * 40:28 — Learning software engineering when agents write the code * 43:31 — Focus, pairing and the art of doing one thing at a time * 45:02 — Combining hand coding, agents and mutation testing in training * 48:36 — Slicing and verification as critical developer skills * 50:41 — Why established Agile practices are gaining new leverage * 51:42 — Vibe-coded applications and choosing an appropriate engineering level * 54:12 — Turning a prototype into production software * 55:23 — A Short Guide to Naming and skills as a new publishing form Key ideas * Agents tend to choose solutions that are mechanically simple and locally safe, but repeated local exceptions can gradually erase a system’s design. * Fast implementation gives XP’s foundations—small increments, testing, refactoring and evolutionary design—new leverage rather than making them obsolete. * Small end-to-end increments keep work demonstrable and preserve opportunities to steer when complexity or consequence demands close control. * Tim stops the agent whenever it proposes a refactor. The transformation may be easy to automate, but deciding whether an abstraction represents something true about the system remains a critical judgement. * Agents, automated tests and Git make ambitious legacy experiments more practical. Work that would once have been too expensive to try can be generated, assessed, discarded or attempted differently. * Tim gives agents an evidence base through skills, architecture recovery and deterministic analysis of Git history, which can expose coupling that source dependencies miss. * Coherent code makes learning profitable: concepts, vocabulary and representations reinforce one another, allowing knowledge acquired in one area to remain useful elsewhere. * Books and articles can become agent skills, bringing established engineering knowledge directly into the coding session while leaving humans responsible for judging its application. * Agents amplify both trajectories. Strong tests, clear concepts and coherent design become easier to reinforce; shortcuts and weak structure can accumulate just as quickly. People and links * Tim Ottinger on Leanpub * Agile Otter blog * Tim Ottinger on X * A Short Guide to Naming * Time for an 8th Virtue: Coherence * LexisNexis * Industrial Logic Tools mentioned * Git * GitHub Copilot * Claude Code * Warp * SonarQube * Consensus This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thenextcommit.substack.com
Sep 8

Ian Johnson: Compose the work. Conduct the flow. Let the agents play.

Episode summary Ian Johnson is a staff engineer at Parento and the author of Harness Engineering. His team can ship six to eight small, reviewable tickets per developer each day—not by trusting an agent to get everything right, but by engineering the system around it. In this episode, Ian explains how precise Jira cards and a truthful project charter control what goes into an agent, while tests, hooks, specialist reviewers and human judgement constrain what is allowed out. We discuss why one card should produce one small pull request, how recurring review feedback becomes part of the harness, why rules must reflect the codebase as it really exists, and how software developers are moving towards “building the thing that builds the thing.” About Ian Ian Johnson is a staff engineer at Parento, where he leads the adoption of agentic software-development practices. He is the author of Harness Engineering, a practical book about building reliable workflows around non-deterministic agents. His work explores how charters, harnesses, verification and deliberate feedback loops can increase delivery speed without surrendering engineering quality or accountability. In this episode * 01:14 — Introducing Ian and Harness Engineering * 02:16 — Ian’s role at Parento and the origins of the book * 03:10 — From Copilot autocomplete to agentic coding * 04:42 — Claude Code, Codex, Pi and parallel agent workflows * 06:34 — Why parallel agents need a carefully engineered system * 08:08 — Review agents, deterministic checks and human accountability * 10:01 — Keeping changes small enough for meaningful review * 11:01 — One Jira card, one pull request * 12:23 — Falsifiable acceptance criteria and explicit exclusions * 13:28 — Using an AI refinement skill without letting it invent requirements * 14:48 — Decomposing a feature into independently releasable work * 17:01 — Shipping six to eight small cards per developer each day * 17:59 — Turning repeated review feedback into harness improvements * 20:20 — The charter as an agreement between the team and its agents * 23:17 — Agents amplify both clean patterns and existing disorder * 24:55 — How Ian structures and indexes project context * 26:32 — Why generic or inaccurate rules make agent output worse * 27:58 — Truthful rules and living migration plans * 29:39 — Refactoring legacy software with characterization tests and TDD * 31:58 — Using 100% coverage as a project-specific safety boundary * 33:10 — Why maintainable code matters more when code is cheap * 35:47 — Keeping humans in the loop to control comprehension debt * 36:57 — Resisting cognitive surrender as a junior developer * 39:33 — Pairing a junior, senior and coding agent * 42:57 — Evaluating charter and harness changes * 45:56 — Raising the developer’s abstraction level beyond code * 47:33 — Building the thing that builds the thing * 51:06 — The amplification thesis and acceptable reliability bands * 53:19 — Why a hook is more powerful than a rule Key ideas * A well-shaped ticket controls what the agent builds; the charter controls how it builds it. * Acceptance criteria should be falsifiable, and out-of-scope boundaries should be explicit. * Small batches preserve flow and comprehension. Ian’s one-card, one-PR rule typically produces a diff of around 250 lines. * A charter must describe the codebase truthfully. When the current architecture and desired architecture differ, the intended change belongs in a migration plan rather than a fictional rule. * Guidance and verification work together: rules feed standards forward, while tests, hooks and analysis feed evidence back. * Repeated review feedback should improve the production system. Ian uses a rule of three before promoting a recurring lesson into the charter or harness. * Agents amplify the environment around them. Good structure and strong standards become easier to repeat; inconsistency and debt accelerate too. * The model cannot own the product or answer a production incident. Humans retain accountability and need enough comprehension to exercise it. * The developer’s work moves towards designing the system that produces the software: shaping work, curating context, strengthening checks and applying judgement. People and links * Ian Johnson on LinkedIn * Ian Johnson on Medium * Ian Johnson on DEV Community * Harness Engineering on Leanpub * Parento Tools mentioned * Claude Code * OpenAI Codex * GitHub Copilot * Orca * cmux * Warp * Jira Thanks for reading! Subscribe for free to receive new posts and support The Next Commit. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thenextcommit.substack.com

8 Episodes

The Next Commit is the forward-looking podcast and newsletter for developers, engineers, and technology leaders focused on building the future of software. thenextcommit.substack.com

Creator

The Next Commit
Years Active

2026
Episodes

8
Rating

Clean
Show Website

The Next Commit

The Next Commit

Episodes

Simon Vane: Giving coding agents a solid foundation

Shashank Patwardhan: Giving agents freedom without giving away control

Matt Erman: Your agent writes the code. You still own the architecture

Kesha Mykhailov: When agents move faster than organisations can think

Quentin Chevrin: When coding shrinks, the developer expands

Michael Tweed: Agents are transforming the engineer’s role. Leadership must change with it.

Tim Ottinger: The new economics of software engineering

Ian Johnson: Compose the work. Conduct the flow. Let the agents play.

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