The short answer
Instruction mode handles requested work, while purpose mode assesses inputs and observations toward an owner-defined intended result through a qualified mission composition. AgentPlat 1.1 supports both without automatically enrolling existing definitions.
Requested work remains useful
A person asks an agent to summarize a document or prepare a proposal. The application supplies the immediate task and existing execution path. This is a natural fit when the deliverable is known and the owner wants to choose each next piece of work.
Existing agents keep their instruction-driven behavior when the new purpose composition has not been enabled. Upgrading coordinated dependencies does not automatically install a scheduler or turn every conversation into purpose intake.
Purpose adds a continuing evaluation cycle
A support agent with purpose mode can receive attributed inceptions and selected signal observations. Through host-supplied scheduling and assessment, those inputs may lead to bounded missions and outcome review. The ongoing direction is explicit, but execution still requires qualified controls.
The adoption guide explains the opt-in composition. Merely selecting a purpose revision cannot start unconstrained work. The host connects identity, stores, assessors, scheduling and effect boundaries before owner activation.
Make mode changes inspectable
The selected way of working is part of a recorded, versioned agent definition. An ordinary message or runtime metadata flag cannot silently change it. An authenticated owner selects the version to use; work stays suspended until its controls have been checked and it is activated again.
See owner configuration for the command path. Returning a governed agent to instruction mode preserves limits, consumed budget and origin; starting an older ungoverned worker is not the supported fallback.
Choose per agent and per responsibility
A Room can combine instruction-driven contributors with a purpose-driven participant when the host composes the required boundaries. For a fully governed shared workspace, connect the required controls for every participant.
Begin with the smallest useful arrangement. Keep a research assistant instruction-driven while piloting a support purpose agent on simulated cases. Compare retained evidence, correction behavior and operational effort. The choice is about how work is initiated and governed, not a ranking in which every application must become maximally autonomous.
Adopt autonomy without redesigning every feature
Many AI products need both immediate assistance and ongoing responsibility. A user may request a document summary while another agent watches an active support case. AgentPlat supports instruction and purpose modes so your team can choose the appropriate work model for each responsibility. This makes incremental adoption possible: retain useful task-based features and pilot governed autonomy where it creates customer value. Evaluate the platform when you need that combination together with persistent history, approvals and recovery, rather than selecting it simply because autonomy is fashionable.
Explore AgentPlat 1.1 adoption when your engineering team is ready to assess the integration, and use the concept series to align product and technical stakeholders on the work model.
Sources and further reading
Documentation reviewed . Consult the linked documentation for current implementation details.