# Seven Commitments

Use this map to translate current loop-engineering vocabulary into seven design commitments for building safer, more useful AI work cycles.

### Market vocabulary map

Current coverage and practitioner glossaries use terms such as agent loop, task loop, control loop, automations, worktrees, skills, connectors, sub-agents, cadence, kill switch, maker/checker, goal-condition primitive, and L1-L3 maturity tiers. The map connects them to the architectural commitments that make a loop safe and useful.

                        Market term
                        How this site uses it
                        Book commitment / layer

                        Agent loop / task loop
                        A repeating AI work cycle that acts, observes, checks, and continues until a stop condition is met.
                        Scaffolding loop; full sensorimotor loop

                        Control loop
                        The older monitor-act-correct pattern that loop engineering adapts to AI agents, tool use, and review gates.
                        Full sensorimotor loop

                        Goal, check command, exit condition, max iterations
                        The operational shape of a well-formed loop: define done, run an objective check, stop on a named result, and cap runaway work.
                        Verifiable stop; materiality gating

                        Goal-condition primitive
                        An independent check that asks whether the loop's stated condition is actually true after an agent turn, rather than letting the acting model grade itself.
                        Verifiable stop; maker / checker

                        Automations / cadence
                        Scheduled or event-triggered loop runs, with frequency chosen by cost, risk, freshness, and response needs.
                        Materiality gating; visible verification

                        Worktrees
                        Isolated working directories or branches that let parallel agent work avoid file collisions and preserve rollback paths.
                        Motor + sensory; visible verification

                        Skills
                        Reusable capabilities or task instructions a loop can invoke instead of re-deriving project practice each run.
                        Long-term memory; debt accumulation

                        Connectors / plugins
                        Tool and API surfaces exposed to the agent through protocols such as MCP or platform-specific integrations.
                        Nervous system; motor + sensory

                        Sub-agents
                        Specialized secondary agents used for parallel work, review, synthesis, or independent verification.
                        Maker / checker; above-individual architecture

                        Memory / state
                        Durable files, trackers, or stores that let loop state survive context windows, crashes, and session boundaries.
                        Externalized memory; consolidation

                        Kill switch
                        A tested mechanism that halts an unattended or long-running loop before cost, permissions, or quality failure compounds.
                        Materiality gating; verifiable stop

                        L1 / L2 / L3 maturity tiers
                        Report-only, assisted patch-only, and unattended loop postures. The tier is an autonomy decision, not a quality score.
                        Materiality dial; human review

Source anchors for this vocabulary include [ADTmag's July 2026 coverage](https://adtmag.com/articles/2026/07/01/loop-engineering-emerges-as-developers-put-ai-coding-agents-on-repeat.aspx), the [MCP.so loop-engineering guide](https://mcp.so/blog/loop-engineering), the [public loop-engineering terminology repository](https://github.com/mdayan8/everything-about-loop-engineering/blob/main/llm-wiki/TERMINOLOGY.md), [loopengineering.wiki](https://loopengineering.wiki/), and current [orchestration/governance tool comparisons](https://www.exemplar.dev/blog/best-ai-agent-loop-tools).

### Related engineering layers

                        Term
                        Boundary

                    Prompt engineeringOptimizes a single instruction or interaction.
                    Context engineeringChooses what files, memory, retrieval, and state the agent can see.
                    Harness engineeringDesigns the tools, permissions, observability, and guardrails around the agent.
                    Loop engineeringDesigns the repeated cycle: goal, state, tools, check, retry, escalation, and stop.

### The seven commitments

- Verifiable stop

- Maker / checker

- Externalized memory

- Materiality gating

- Frame before solve

- Visible verification

- Debt accumulation

Together, the commitments frame the work, preserve state, scale effort to stakes, separate making from checking, verify progress, stop deliberately, and record what the loop learns. Put them into practice with the [prompts](/prompts/), [worked examples](/examples/), and [living matrices](/matrices/).

The full framework

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