03.1
Confidence is not understanding
A fluent answer can look certain while missing the scope, dependencies and contradictions that determine whether a software claim is actually useful.
Harten Papers
Evidence, control and software change.
Research and field thinking on modernisation, AI-assisted engineering and human authority.
Read the paper, inspect the evidence and follow the implications for changing complex systems.
03
AI needs evidence, not confidence.
AI systems can produce convincing answers without establishing why those answers should be trusted. Evidence connects claims to sources, scope, uncertainty and the decisions that depend on them.
Case study 01
From unfamiliar legacy code to a reviewable application picture
A public benchmark using NightingaleHQ's MIT-licensed mixed legacy example corpus: what MaaS could establish in approximately an hour, what it left unresolved, and why the gaps matter.
Empirical follow-on — 16 September 2026
Evergreen engineering beyond the context window
A live engineering case linking sustained work, accountable change and continuous renewal beyond individual model contexts.
02.03
Modernisation starts by establishing what is actually true
Discovery should distinguish what an application does, what the organisation needs and what it has authorised someone to change. That distinction connects application understanding to a defensible modernisation decision.
Technical paper — 04
Establishing completion in autonomous multi-agent systems
An empirical case study of evidence-bearing distributed termination: how autonomous engineering agents can establish that shared work is complete rather than merely report that their local task has finished.
Technical paper — 03
Coordinating autonomous software agents under shared mutable state
An empirical case study of autonomous software-engineering agents coordinating shared mutable state through explicit invariants, state-dependent evidence and minimum necessary serialisation.
Research 01
Recursive Application Denoising
An evidence-directed approach to reconstructing complex software systems, compressing application discovery and directing human attention to the uncertainties that still require judgement.
Working observation
Six hours of work, and a decision that remained mine
Roughly six hours of essentially autonomous engineering execution, with human review and decisions on direction. A working observation from building Harten.
02.02
Legacy systems are expensive because knowledge has decayed
Legacy systems become expensive to change when the organisation loses reliable knowledge of how they actually work, where behaviour lives and what a change may affect.
Standalone
Context is not memory
Long-running AI systems need durable state outside the model context if decisions, evidence and engineering intent are expected to survive compaction and continue reliably over time.
02
Poor productivity is often a function of legacy
What appears to be poor engineering productivity is often the cost of changing a system the organisation no longer understands well enough.
01.3
You cannot govern what you cannot understand
Safe autonomous change depends on evidence-backed understanding across code, operations, people and policy. That understanding must preserve provenance, uncertainty and conflicting perspectives.
01.2
The human cannot be the bottleneck
Human oversight cannot scale by requiring a person to inspect every machine action. Human authority must scale by concentrating judgement where evidence, uncertainty and consequence require it.
01.1
Speed is not control
AI makes software production dramatically faster, but velocity does not establish whether a change is understood, justified or safe. As generation gets cheaper, confidence becomes scarce.
01
AI has made code cheaper. It has not made change safe.
AI is reducing the cost of producing software change. The scarce capability is increasingly the evidence, authority and control required to decide which changes are justified.