Adaptive Product Development

After Agile

Organisations built for slower feedback loops cannot operate at AI speed.

AI increases output. Most organisations still cannot sense, decide, and adapt fast enough to safely absorb it.

Adaptive Product Development is the operating model for teams building with AI. The SODA Loop — sense, orient, decide, act — is the runtime mechanism inside it.

Explore the SODA Loop → Read the manifesto

The SODA Loop

Sense. Orient. Decide. Act.

Adaptive Product Development is the umbrella philosophy. The SODA Loop is the operational mechanism inside it. Four phases, repeated continuously, in production.

The SODA Loop: Sense, Orient, Decide, Act, repeated continuously.

  1. 01 Sense

    Collect runtime signals from users, systems, agents, workflows, and outcomes.

  2. 02 Orient

    Interpret context, align priorities, evaluate constraints, and understand risk.

  3. 03 Decide

    Coordinate human and AI judgment to determine the next best action.

  4. 04 Act

    Execute through products, workflows, automations, teams, and systems.

The Manifesto

We are uncovering better ways of building products, organisations, and intelligent systems by operating them live, learning from them continuously, and helping others do the same.

Through this work we have come to value:

  • continuous feedback loopsoverfixed delivery cycles
  • trusted learning systemsoverstatic releases
  • runtime evidenceoverupfront certainty
  • governed autonomyoverunchecked automation
  • shared contextoverdisconnected tooling
  • human judgment
    amplified by AI    overlabour substitution
  • evidenceoveroutput optimism
  • evolution in operationoveroptimisation before launch

That is, while there is value in the items on the right, we value the items on the left more.

The historical arc

  1. Waterfalloptimised for predictability.
  2. Agileoptimised for discovery.
  3. DevOpsoptimised for deployability and outcomes.
  4. Adaptive Developmentoptimises for runtime learning loops.

Before. After.

Side-by-side. On the left, the static loop: Build, Ship, Wait, Review, with quarterly or sprint-scale feedback. On the right, the SODA Loop: Sense, Orient, Decide, Act, with continuous runtime adaptation. Organisations built for slower feedback loops cannot operate at AI speed.

Why now

Agile is not wrong. It is incomplete for adaptive AI systems.

Products no longer only execute predefined logic and wait for the next release cycle to improve. They can recommend, generate, personalise, decide, and trigger actions while live. They can fail in non-deterministic ways. They can drift. They can absorb bad context. They can create value quickly and create risk just as quickly.

The unit of work moves from shipped features to governed learning loops.

The most valuable organisations will not simply be the ones that ship faster. They will be the ones that build trusted loops between users, products, data, models, agents, and human decision-makers. They will treat context as infrastructure and evals as product work. Raw coding throughput is not productivity. Governance and observability stop being late-stage controls; they become how the product is designed.

Adaptive Product Development is the operating model for organisations that learn while operating. The SODA Loop — sense, orient, decide, act — runs continuously in production, with governed autonomy and human judgment in the loop.

The twelve principles

Behind the manifesto.

  1. 01Put real-world outcomes above model novelty.
  2. 02Every adaptive behaviour is a product decision.
  3. 03Make evals part of development, not an afterthought.
  4. 04Keep the blast radius of autonomy explicit and limited.
  5. 05Design for rollback before scale.
  6. 06Prefer the simplest agent pattern that works.
  7. 07Give models governed access to the context they need.
  8. 08Make runtime behaviour visible to humans in detail.
  9. 09Track learning velocity, not just delivery velocity.
  10. 10Keep humans responsible for exceptions and judgment.
  11. 11Build internal platforms that make the safe path the fast path.
  12. 12“Done” is a temporary operating condition, not an endpoint.

The operating model

Eight layers of governed continuous evolution.

Adaptive Product Development is not a single practice. It is a stack of eight layers that together keep a live system safe, observable, and able to learn.

  1. 01 Live feedback architecture

    Instrumented user signals, behavioural events, qualitative feedback, task outcomes, error categories, and economic metrics, connected back to roadmap, model choice, and workflow design.

  2. 02 AI evaluation layer

    Offline and online evals, golden datasets, regression tests, rubric scoring, and human review for high-stakes cases. Behaviour specified, not just functionality.

  3. 03 Context layer

    Shared organisational memory, retrieval governance, documentation freshness, repository-level instructions. AI quality is now a context problem as much as a model problem.

  4. 04 Agentic workflow layer

    An autonomy matrix by risk class. Action registers, tool permission maps, fallback paths, and escalation policies. Define where agents act, where humans decide, where escalation is mandatory.

  5. 05 Governance and trust layer

    Risk tiering, least privilege, audit trails, incident playbooks, rollback rights, model and agent registers. Governance is the runtime permission model of the system, not a late legal review.

  6. 06 Observability layer

    Traces across prompts, tools, retrieval, outputs, human feedback, costs, and downstream effects. The system can be technically healthy while outputs quietly degrade.

  7. 07 Product adaptation layer

    Where learning changes the product. Personalisation, recommendation shifts, workflow handoffs, prompt and retrieval improvements, feature evolution. Adaptation backlog and outcome reviews.

  8. 08 Organisational operating model layer

    Decision rights across product, engineering, platform, data and AI, risk and compliance. Funding for platform and governance, not only visible features. Rituals that connect roadmap, operations, incidents, and learning.

Authored by

Marton Gaspar

Product leadership coach. I work with senior product, engineering, and design leaders. A hundred coached so far. Before coaching full-time: led product at venture-backed scale-ups, founded three startups, ten years building AI products.

Adaptive Product Development is the operating model I see emerging across the teams I coach and the products I help build. This site is the public-good version: manifesto, principles, model, library. Permissively licensed (see below).

Lineage. The framework draws on Marty Cagan's product operating model; Martin Fowler's writing on continuous integration and software architecture; the DevOps and continuous-delivery research of Nicole Forsgren, Jez Humble, and Gene Kim; Charity Majors's observability practice; the AI-engineering writing of Simon Willison and Andrej Karpathy; the agent-engineering practice emerging at LangChain, OpenAI, and Anthropic; and DORA's research on team performance. Cited as influences, not endorsements.

Apply this with me at martongaspar.com.

Coming next

  • The Adaptive Readiness Diagnostic. A self-serve assessment across the eight layers.
  • The library. Flagship essays starting with Why AI does not just make Agile faster.

Licence

Cite this. Build on it. Attribution required.

This work is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). You are free to share, adapt, and build on the manifesto, principles, and operating model, including for commercial use, on the condition that you give appropriate credit and link back to adaptiveproductdevelopment.com.

Suggested citation
Gaspar, M. (2026). Adaptive Product Development.
https://adaptiveproductdevelopment.com

Version 1.0, May 2026. No warranty. The framework is offered as a thinking tool, not a guarantee of outcome.