Manage by Objective (not by Method)
Drucker's 1959 principle applied to directing AI agents: because the worker is out-known about the specifics, you supply a goal it can test itself against rather than prescribing the method, so the stake lives in the objective rather than the worker.
Also known as: manage by objective, managing by objective, a goal not a task, hand the model a goal, the Drucker inversion, manage by objective, not by method
Part of: Human as Orchestrator of Agents
How authors use it
Oldest first (extraction date when no publication date is known).
| Author | Their term | Their meaning |
|---|---|---|
| What Survived Contact Sam Ruby · 2026-07-01 | manage by objective (not by method) | Drucker's 1959 principle applied to agents: when the worker is out-known about the specifics, you supply a goal it can test itself against rather than prescribing how to do the work |
Documents (5)
- What's an Orchestrator—and Why Does Software Need One? Tim O'Brien · 2026-08-17 source ↗ Reader 🖥️
Software development is splitting the 'developer' role into an 'orchestrator' — a generalist who directs delegated-intelligence systems, remains accountable for what they produce, and can support them without AI — and this role should be professionalized (apprenticed, certified, insured) the way medicine and engineering are, because its holders now carry outsized responsibility without the licensing structures that responsibility normally requires. - What is “loop engineering?” The Pragmatic Engineer · 2026-07-14 Reader 🖥️
“Loop engineering” — designing the orchestration around an AI coding agent (goals, completion checks, state persistence, fresh-context iteration) rather than hand-writing one-off prompts — is a real practice that originated as a DIY workaround (the “Ralph loop”) for small context windows, but by mid-2026 major harnesses had absorbed most of it into a native /goal primitive, so for most developers besides those building AI infrastructure, it is a largely-already-commoditized skill rather than a durable new discipline. - Adam Mosseri: AI is a tailwind for authenticity Adam Mosseri (guest); Lenny Rachitsky (host) · 2026-07-11 Reader 🖥️
Because AI makes creation nearly free and floods the world with synthetic content, the scarce and durable human advantages become taste, judgment and authenticity — which Mosseri argues is a tailwind rather than a headwind for Instagram (audiences will seek out real people and creators, not less), and which in turn reshapes how work is organized: teams compress into small generalist 'pods' led by a new 'product staff' role, while hiring rewards curiosity, grit and a willingness to try over deep specialization. - Slack Introduces Agent Driven End-to-End Testing to Improve Resilience in UI Test Automation Leela Kumili · 2026-07-10 Reader 🖥️
Agentic testing — AI agents that execute end-to-end tests from a high-level intent rather than fixed scripts, adapting around superficial UI or service changes instead of failing — reduces brittle-test maintenance and belongs as a complementary top layer of the test pyramid, reserved for debugging and exploratory use because of cost, while deterministic tests remain the primary mechanism for validating critical logic. - What Survived Contact Sam Ruby · 2026-07-01 source ↗ Reader 🖥️
When agents write the code, rigor does not vanish but migrates to the objective and its evaluation; directing an agent is therefore a management act (a hire, not a tool-use) whose one non-delegable duty is owning the objective and the oracle that checks it.
Propositions touching this concept (2)
By publication month, oldest first (extraction month when no publication date is known).
2026-07
- explicitWhen a manager reaches for an LLM instead of routing work to the team that reports to them, they have made a hire, not picked up a tool — so the 'may non-engineers steer the model?' permission question dissolves into 'do they know how to manage by objective?'
- explicitcorroboratedDirecting an agent well means managing by objective rather than by method (Drucker, 1959): because the director is out-known about the specifics, they should supply a goal the agent can test itself against instead of prescribing the method.
Tensions
How far can day-2 operations be handed to self-healing / autonomous remediation?
from: What did the participants of the Thoughtworks 'Future of Software Engineering' Europe retreat (mid-2026) conclude about how agentic AI is reshaping software engineering?
- Looking back at the Future of Software Engineering unconference (Bartosz Ocytko): A self-healing platform foundation should make whole classes of failure 'disappear', with increasingly autonomous background 'guardrail' agents refactoring, adding tests, and protecting invariants.
- AI Is Changing Software Engineering. The Fundamentals Still Matter. (Abby Bangser): Detection, context, and suggestion are safe wins, but automated remediation must remain an intentional, context-specific decision, not a default end state.
- What Survived Contact (Sam Ruby): Self-healing has, in practice, only moved halfway: diagnosis is automated while remediation is deliberately withheld from the machine.
Assessment: Ruby's report is descriptive (where practice actually stands) and Ocytko's is aspirational (where platforms could go); the gap between 'remediation is withheld' and 'failure modes disappear' is precisely the distance the optimists are projecting across. Bangser supplies the reason the gap may not close on efficiency grounds alone — the withheld human step is also the learning step.
Is the value question genuinely settled by evidence, or does uncertainty and sampling bias still dominate?
from: What did the participants of the Thoughtworks 'Future of Software Engineering' Europe retreat (mid-2026) conclude about how agentic AI is reshaping software engineering?
- Notes from FOSE Europe (Giles Edwards-Alexander): The evidence is in; the value is clearly here and here to stay.
- Finding Comfort in the Uncertainty (Annie Vella): Nobody has it figured out; there is more uncertainty than certainty, and the shared set of questions is the real outcome.
- What Survived Contact (Sam Ruby): 'Rigor migrates' is about as settled as this young field gets, but the room is a non-random, incumbent-tilted sample, and the doubt is currently better evidenced than the hope.
Assessment: The single sharpest divergence, and it sits directly under the headline whether-to-how convergence: they narrate the same shift but disagree on what it establishes. Edwards-Alexander and Herlein read the room's confidence as evidence; Vella and Ruby read the same room as a self-selected in-group. Ruby has the better of it methodologically — he is the only one to interrogate the sample and to notice the evidence is asymmetric (upside anecdotal, downside studied), which is uncomfortable precisely because the cohort's confidence is built on the weaker side.
Where is the locus of safety for non-engineers / citizen developers building software?
from: What did the participants of the Thoughtworks 'Future of Software Engineering' Europe retreat (mid-2026) conclude about how agentic AI is reshaping software engineering?
- Looking back at the Future of Software Engineering unconference (Bartosz Ocytko): In a tightly-constrained self-healing platform — not universal upskilling.
- What Survived Contact (Sam Ruby): In the director's management skill — a non-engineer steering an agent has made a hire, and the requirement is that they can manage by objective.
- AI Is Changing Software Engineering. The Fundamentals Still Matter. (Abby Bangser): In API-first, MCP-accessible tooling that supports rather than restricts the new builders.
- Why did Martin Fowler invite 60 engineers to a ski resort? (Ivett Ördög): It is genuinely unsolved — the tension between democratisation and its risks is an open question.
Assessment: Four different answers to the same governance question: the platform (Ocytko), the director's discipline (Ruby), the tooling interface (Bangser), or nobody-knows-yet (Ördög). Ruby and Ocytko are the true opposites — Ruby puts the burden on a human management skill, Ocytko designs the human out by making failure modes disappear in the platform. That both are held by attendees of one retreat is a measure of how unsettled the citizen-developer problem is.
Is code a durable artifact to be maintained, or an ephemeral byproduct regenerated from a more durable source (spec/objective)?
from: What is the future of software development in the agentic/AI age?
- We've Stopped Arguing About Whether AI Changes Software (Greg Herlein): Code is (or is becoming) ephemeral — a disposable byproduct the agent regenerates from the spec; the spec is 'the point.'
- Fragments: February 4 (Martin Fowler (Feb 4)): When coding is free, the specification-plus-tests becomes the durable, primary engineering artifact.
- Software Design in the Agentic Age: Place Your Bets (Mathias Verraes): Specifications could replace code as the source of truth, but throwaway regeneration is a bet he is unwilling to make; keep code durable and revertible.
- Why did Martin Fowler invite 60 engineers to a ski resort? (Ivett Ördög): Code endures because the winning agents behave like engineers moulding a maintained codebase, not compilers regenerating from scratch.
- What Survived Contact (Sam Ruby): The durability question is a category error — what endures is neither the code nor the prose spec but the objective and its oracle.
Assessment: The most-cited open question of the corpus, and genuinely unresolved (Herlein: 'no consensus here. None.'). The 'code is the better spec' counter is strong — code is unambiguous, executable, deterministic, whereas a prose spec is reinterpreted each run, which sits in tension with verifiability being the quality bar. Ruby's move is the most defensible: it dissolves the code-vs-spec fight by locating durability in the evaluation rather than any representation. Verraes and Ördög expose the load-bearing empirical unknown — nobody has data on regenerate-vs-patch cost or on long-term evolvability of agent-maintained code.
Where does the non-delegable human asset now live — the harness/platform, the oracle, the fundamentals, or the developer's understanding?
from: What is the future of software development in the agentic/AI age?
- Notes from FOSE Europe (Giles Edwards-Alexander): In the harness — the continuously-evolving scaffolding of architecture guidance and rigour around the agents — though it must stay team-distributed, not centralized.
- What Survived Contact (Sam Ruby): In the oracle — the objective's evaluation is the single thing the person directing the model cannot delegate back to it.
- Jimmy Nilsson's Post — reflections after the Thoughtworks 'Future of Software Development' retreat (Jimmy Nilsson): In the fundamentals plus a renewed 'Extreme Humanism' — the durable disciplines and the conspicuously human element.
- AI Is Changing Software Engineering. The Fundamentals Still Matter. (Abby Bangser): In right-sized boundaries and the operational loop — humans must stay in operations because their learning is the irreplaceable asset.
- Fragments: February 9 (Martin Fowler (Feb 9)): In the developer's model-building and shared understanding — the craft of devising abstractions to reason about the domain.
Assessment: This is the deepest structural disagreement — every author agrees a human residue remains but nominates a different residue, and the choice determines what to invest in and what to teach. The harness-centric (Edwards-Alexander), oracle-centric (Ruby), fundamentals-centric (Nilsson/Bangser), and understanding-centric (Fowler) camps are not merely emphases: they imply different career ladders, different hiring, and different failure modes. Ruby's oracle and Bangser's operational-learning are the most operationally concrete; Nilsson's is the least argued. The absence in the corpus of anyone who holds 'nothing is non-delegable' is itself telling (see unexamined assumptions).
How far should software be automated — toward the fully-autonomous dark/software factory, or held back to bounded autonomy with humans in the loop?
from: What is the future of software development in the agentic/AI age?
- Why did Martin Fowler invite 60 engineers to a ski resort? (Ivett Ördög): Full automation is genuinely viable in context and she is 'ironically' being pulled toward the dark-factory camp.
- Looking back at the Future of Software Engineering unconference (Bartosz Ocytko): The aspirational endpoint is a largely self-evolving software factory; humans build and operate the backbone rather than the application.
- AI Is Changing Software Engineering. The Fundamentals Still Matter. (Abby Bangser): Bounded autonomy — keep humans in the operational loop; automated remediation must stay an intentional decision.
- What Survived Contact (Sam Ruby): Self-healing has stalled halfway on purpose: diagnosis is automated, remediation withheld from the machine.
- Software Design in the Agentic Age: Place Your Bets (Mathias Verraes): Dark factories are safe only for orgs that already have high test coverage, skilled engineers, and a quality culture; most should cover fundamentals first and stay revertible.
Assessment: The spectrum runs from Ördög/Ocytko (full automation is arriving and desirable) to Bangser/Ruby/Verraes (deliberately withhold the last step). The decelerationists have the better of the safety argument today — Ruby's 'nobody will cede the blast radius' and Bangser's 'removing humans removes learning' identify concrete, unmet prerequisites — but Ördög's context-dependence (backends, COBOL) shows the boundary is domain-specific, not absolute. The real disagreement is less about capability than about who absorbs the blast radius when an autonomous fix goes wrong.
Does the current evidence favor optimism or doubt about agentic engineering's payoff?
from: What is the future of software development in the agentic/AI age?
- Jimmy Nilsson's Post — reflections after the Thoughtworks 'Future of Software Development' retreat (Jimmy Nilsson): Optimistic: the mature, nuanced, human-centred discourse among leading practitioners is a good predictor of a healthy trajectory.
- The Future Of Software Engineering (Thoughtworks): Pessimist-realist: there is an active regression underway that most optimism ignores.
- What Survived Contact (Sam Ruby): Doubt is currently better-evidenced than hope, and that asymmetry is the uncomfortable thing to watch.
Assessment: The sharpest divergence in the corpus, because it is about the epistemic ground of every other claim. Nilsson's optimism is explicitly consensus-of-the-in-group and self-flagged as biased; Ruby and the Thoughtworks report both bring the only genuinely external evidence (DORA reversal, HBR intensification, outside surveys) — and it points the other way. On the current record, the skeptics have the stronger footing precisely because the optimistic case is methodologically weakest (a 'sample of one,' as Ruby says of his own best data). The whole cohort's cautious optimism may be an artifact of who was in the room.
Open gaps (5)
- incompleteThe article's evaluative back half (sections 5–7: cost/'tokenmaxxing' disappointment stories, the 'was it just a hack' argument, and the context-engineering conclusion) is paywalled; this extraction only had the one-line table-of-contents previews for that material, not the supporting evidence or full argument.What is “loop engineering?” — read next: Get subscriber access to read the full piece, especially the cost-tradeoff stories in section 5 and the closing argument in section 7 for why context engineering may matter more than loop engineering — directly relevant to the persona's 'rigorous adopter, not hype-follower' stance on AI-agent practice.
- incompleteThe whole cross-retreat comparison rests on an acknowledged seam: the author read only the public summary of the earlier retreat, so he cannot separate 'the conversation genuinely grew' from 'the earlier summary simply compressed this out.' The central 'what changed' claim is offered on face value with that limitation showing.What Survived Contact — read next: Obtain the fuller record (not just the public key-takeaways summary) of the earlier Thoughtworks retreat and re-run the comparison against like-for-like source material.
- incompleteThe article never specifies the accountability mechanism when multiple orchestrators, or an orchestrator and a vendor/platform, jointly produce a system that fails — it only asserts that delegated intelligence is 'always traceable back to an accountable operator' without addressing shared or contested authorship, which is the common real-world case (an orchestrator directing a third-party agent platform built by another company).What's an Orchestrator—and Why Does Software Need One? — read next: Multi-party AI liability and supply-chain accountability frameworks (e.g. the enterprise-as-accountable-integrator argument in extract-outsourced-ai-still-own-the-risk); agent identity/attribution mechanisms that bind actions to a specific accountable principal.
- illogicalThe '100x productive, 100x responsible' claim underlying the insurance/licensing prediction is a bare assertion with no supporting mechanism — it isn't clear that liability scales linearly (or at all) with output volume rather than with the severity and irreversibility of specific decisions, which is how liability actually tends to be assessed (e.g. malpractice law weighs harm caused, not cases handled per year).What's an Orchestrator—and Why Does Software Need One? — read next: Legal/insurance literature on how liability and malpractice exposure are actually assessed for high-throughput professionals; the clinical-vs-legal-standards-of-proof distinction between diagnostic usefulness and legal culpability.
- incompleteThe article asserts that finishing and delivering software 'takes the same effort, energy, and technical expertise... as it always has,' which sits in tension with its own DRM anecdote showing a months-long task compressed to 40 minutes — it never reconciles why orchestration effort is claimed to be unchanged in kind when the article's own evidence shows it collapsing by roughly two orders of magnitude in time.What's an Orchestrator—and Why Does Software Need One? — read next: Measured before/after productivity studies of AI-assisted delivery (e.g. time-to-10th-PR, throughput metrics) that could clarify whether 'effort' and 'elapsed time' are being conflated here.