Self-Modification

The fourth required capability of an APM agent (sources/2026-calvanese-agentic-bpm-manifesto, §3.4): the ability of agents and agent coalitions to adapt and evolve over time. Self-modification is grounded in the agent's perception abilities and enables continuous improvement toward self-improving back-ends for organisations.

Fundamental distinction: adaptation vs evolution

Axis	Adaptation	Evolution
Scope	Single process instance	Across instances and contexts
Duration	Short-term, ephemeral	Long-term, persistent
Permanence	Does not alter the process model	Alters process logic / model / policy
Knowledge type	Localised, instance-specific	Aggregated, systemic patterns
Enactment	Individual or collaborative at runtime	Shared among multiple agents
Trigger	Reactive (deviation) or proactive (prediction)	Learned improvement opportunities

Feedback cycle

Ideally, adaptation and evolution form a feedback cycle in the APM system: consistently effective adaptations become candidates for evolutionary incorporation into the process model — and thus into the agent's frame (see concepts/framed-autonomy).

Conversely, evolution should reduce the frequency of certain adaptations by pre-emptively addressing known failure modes.

Design implications

Adaptation mechanisms prioritise responsiveness, robustness, safety under uncertainty. Require runtime monitoring, exception handling, flexible execution engines.
Evolution mechanisms require pattern mining across execution histories, causal inference, statistical validation, and change management protocols.

Risks

Poisoning — self-modification may evolve agents into unsafe behaviour if learning signals are corrupted.
Uncertainty quantification and "learning-to-defer" approaches are needed to know when to return control to human agents.

Elaboration in Elyasaf et al. 2025

sources/2025-elyasaf-self-modifying-abps operationalises the distinction with:

A 5-dimensional classification (adaptation/evolution; task/flow/process; reactive/proactive; human-driven/autonomous; planned/emergent).
A three-level autonomy roadmap (concepts/abps-autonomy-levels).
The MAPE-K loop (concepts/mape-k-loop) as the canonical adaptation architecture.
Explicit research challenges on governance, continual learning, and uncertainty quantification.

concepts/agentic-bpm · concepts/framed-autonomy · concepts/abps-autonomy-levels · concepts/mape-k-loop · concepts/agent-process-observability · methods/agent-oriented-process-mining