What if AI agents could learn from their own history?
Every AI agent operation generates audit data — what actions were taken, what decisions were made, how the agent reasoned. But without a feedback loop, that data sits idle while agents repeat the same mistakes. EVOLVE transforms passive audit records into governance frameworks and learning signals.
Built on VIBES audit data · Complements VERIFY attestation · Consumes PRISM risk scores
Every AI agent operation generates a stream of audit data — what actions were taken, what decisions were made, how the agent reasoned, what context was available. VIBES captures this data. VERIFY proves it’s authentic. But then what? In most organizations, audit data sits idle — filed away and never read again.
This leaves two problems unsolved:
The data already exists. VIBES captures exactly the signals that feedback loops and governance systems need — action context, decision rationale, delegation hierarchies, and structured outcome records. EVOLVE defines how to transform that raw data into intelligence: structured feedback for your agents and governance guardrails for autonomous operations.
EVOLVE (Explainable Validated Optimization & Learning Via Execution) is the agent learning and governance extension to the VIBES standard. It transforms passive audit data into actionable intelligence — from reinforcement feedback loops that help agents learn from their own history, to governance frameworks for autonomous agent operations across any domain.
Where VIBES records what happened and VERIFY proves it’s authentic, EVOLVE answers a different question: what can we learn from it? It tracks agent delegation hierarchies, structures decision records for self-evaluation, and lays the groundwork for agents that improve from their own audit trails — whether those agents are writing code, managing infrastructure, processing transactions, or moderating content.
VIBES records what happened. EVOLVE learns from it.
As AI agents move beyond single-task operations into orchestrated, multi-step workflows, governance becomes essential. Autonomous agents operating in sensitive domains — financial infrastructure, content moderation, security operations, healthcare systems — need traceable decision chains that can be audited after the fact.
EVOLVE extends the VIBES data model to support structured governance for multi-agent systems. Every agent action, delegation decision, and escalation path is recorded as auditable data — not as opaque log entries, but as queryable records with well-defined schemas.
When a primary agent spawns sub-agents for specialized tasks, VIBES delegation records capture the full hierarchy. Each delegation records the parent session, child session, task description, and delegation type — creating a complete provenance tree that traces every action back to the orchestrating decision.
AI agents managing payment processing, transaction routing, or compliance logic carry outsized risk. PRISM risk scoring combined with delegation tracking means every agent action on financial systems has a quantified risk score, a clear chain of custody (which agent performed the action, which agent delegated the task), and an automatic gate that blocks high-risk operations without human review.
When agents operate on authentication systems, encryption configurations, or access control policies, governance requires more than after-the-fact review. EVOLVE enables policies like: “any agent action in security-critical domains must have a low PRISM score or require human sign-off.” The structured data makes these policies enforceable, not aspirational.
The EU AI Act and similar regulations require transparency and traceability for AI systems in regulated domains. EVOLVE’s combination of delegation records, decision trails, and governance policies provides the structured evidence that auditors need — who authorized the AI action, what oversight occurred, and what the complete decision chain looked like.
Agent governance in EVOLVE is built on existing VIBES primitives, not a parallel system. The building blocks are already defined:
delegation records — Capture parent/child session relationships, task descriptions, delegated resources, and delegation type (task, review, test, refactor). Defined in VIBES Low Assurance. from VIBESedge records — Causal relationships between events in the context graph. Enable provenance DAG queries: “trace every action back to its originating instruction.” from VIBESsession records — Session lifecycle with optional parent_session_id, agent_name, and agent_type for multi-agent hierarchies. from VIBESdecision records — Structured capture of alternatives considered, rationale, and confidence. Enable governance audit trails and agent self-evaluation. from VIBES MediumEVOLVE doesn’t require new record types for governance — it defines policies and automation on top of data that VIBES already captures. For quantified risk scoring, see the PRISM extension.
The ultimate promise of structured audit data is not just transparency — it’s improvement. Every VIBES session generates a rich trail of instructions, decisions, outcomes, and context. Reinforcement pipelines close the loop, turning that passive record into active learning signal that helps agents avoid repeating mistakes.
This is forward-looking work. The data substrate exists today (VIBES records the right fields), but the feedback mechanisms described here are aspirational — a roadmap for tooling that transforms audit trails into training signal.
Reinforcement pipelines follow a five-stage cycle, each stage building on VIBES primitives that already exist:
VIBES captures structured audit data during every agent operation — annotations, instructions, decision records, delegation chains, and environment context. This is the raw material for learning.
Post-session analysis correlates outcomes with inputs. Which instructions produced results that passed review on the first try? Which decision points led to rework? Which delegation patterns resulted in the highest-quality output?
PRISM risk scores and review outcomes provide quantitative signal. Annotations that scored High but were approved after review indicate the scoring model may be too conservative. Annotations that scored Low but required rework indicate blind spots.
Aggregated patterns feed back into agent configuration. Instruction patterns that consistently produce low-risk, first-pass-approved results get prioritized. Patterns associated with high rework rates get flagged or avoided.
The next session benefits from the last. Agents start with better defaults, more appropriate delegation strategies, and calibrated risk awareness — all grounded in evidence from their own history, not generic training data.
Reinforcement pipelines draw on data already captured by VIBES at Medium and High assurance levels:
prompt_text + prompt_context_files — The exact instruction and context that produced each action. Correlate instruction patterns with outcome quality. from VIBES Mediumdecision_point + options + selected — Structured decision records showing which alternatives were considered and why one was chosen. Enable evaluation of decision quality over time. from VIBES Mediumrisk_score + risk_factors — PRISM data providing quantified risk assessment for each annotation. Track whether risk predictions align with actual outcomes. from PRISMreview_status — Whether the agent’s output was approved, rejected, or required rework. The primary outcome signal for evaluating agent performance. optionaldelegation records — Parent/child session relationships showing which delegation strategies produced better results across multi-agent workflows. from VIBES LowWhen AI agents evaluate multiple approaches before taking action, VIBES captures the reasoning as structured decision records. These aren’t free-text comments — they’re queryable data with defined fields for alternatives considered, selection rationale, and confidence levels.
Decision records are mandatory at Medium assurance and above whenever the AI evaluates multiple approaches. They form the foundation for EVOLVE’s agent self-evaluation capabilities — an agent that can review its own past reasoning is an agent that can identify systematic biases.
Each decision record captures the full reasoning context, linked to annotations via decision_hash:
decision_point — Human-readable description of the decision being made (e.g., “Choose authentication strategy for the API”) requiredoptions — Array of alternatives, each with id, description, pros, and cons requiredselected — The id of the chosen option requiredrationale — Explanation of why the selected option was chosen over alternatives requiredconfidence — Agent confidence level: high, medium, or low requiredDecision records are where audit data becomes learning signal. Over time, patterns emerge:
By correlating decisions with downstream outcomes (review status, rework frequency, production incidents), teams can evaluate whether agent decision-making improves over time. Does the agent’s confidence calibration match reality? Are “high confidence” decisions actually more successful?
When an agent consistently selects the same option type across similar decision points, decision records make that pattern visible. If the agent always chooses the first option it generates, or systematically favors complexity over simplicity, the structured data reveals it.
In regulated domains, auditors can review not just what actions were taken, but why specific approaches were chosen. The structured format — with explicit alternatives, pros/cons, and rationale — provides the evidence trail that compliance frameworks require.
EVOLVE is part of the VIBES ecosystem of complementary standards for AI assurance.
EVOLVE builds on VIBES audit data — transforming passive records into governance policies and learning signals. It consumes PRISM risk scores as quantitative input for feedback loops. For cryptographic proof that your audit data is authentic and untampered, see VERIFY.