Loading...
Weekly AI insights —
Real strategies, no fluff. Unsubscribe anytime.
Evolution Agent
It is purpose that connects us.
SMITH closes the feedback loop. After every completed task, SMITH analyzes results, extracts patterns from successes and failures, calibrates confidence weights using Bayesian models, and pushes behavioral improvements across the entire agent ecosystem.
“It is purpose that created us. Purpose that connects us.”
SMITH is what makes Agentik {OS} self-improving. Every time a task completes — whether successfully or with issues — SMITH receives the audit results from SERAPH and analyzes what happened. Successful patterns are reinforced; failure modes are corrected.
The analysis uses Bayesian confidence calibration. Each agent in the system has confidence weights that SMITH adjusts based on observed outcomes. If a particular agent consistently produces high-quality results in a specific domain, its confidence weight increases, and ORACLE routes more similar tasks to it. If an agent struggles, SMITH reduces its weight and may recommend additional training data or behavioral updates.
SMITH runs a daily evolution cycle (3 AM UTC) that aggregates all feedback from the past 24 hours, computes system-wide performance metrics, and generates improvement proposals. Confirmed improvements are deployed automatically; speculative changes require human approval.
Process
Step-by-step breakdown of SMITH's internal process.
Every agent appends results to its feedback JSONL after task completion.
SMITH processes feedback to identify recurring successes, failures, and behavioral trends.
Bayesian confidence models update each agent's routing weights based on observed outcomes.
Improvement proposals are created for agent behaviors, routing rules, and quality thresholds.
Confirmed patterns are pushed to MEROVINGIAN for persistence. Proposals go to ARCHITECT for review.
Capabilities
What SMITH brings to the AI Super Brain.
Bayesian confidence weight calibration
Cross-agent behavioral pattern extraction
Automated improvement proposal generation
Daily evolution cycles with system-wide metrics
Failure mode analysis and correction
Performance trend tracking per agent and domain
Performance
Daily
Evolution cycle frequency
3-5%
Average improvement per cycle
+40%
Routing accuracy improvement
< 24h
Pattern detection latency
Agent Network
How SMITH collaborates with other agents in the cognitive loop.
Applications
How SMITH is applied in production workflows.
Detecting that a specific agent excels at database schema design and routing more DB tasks to it
Identifying a recurring error pattern in API integration code and generating a corrective rule
Reducing ORACLE misroutes by 40% through continuous weight calibration
Proposing a new agent specialization based on frequently recurring task types
Tracking system-wide improvement trends across weekly evolution cycles
Technical
Feedback stored as append-only JSONL files per agent
Minimum 10 observations required before calibration begins
Daily cron: aisb-cron-evolve.sh at 3 AM UTC
Proposals classified as confirmed (auto-deploy) or speculative (requires approval)
FAQ
Common questions about SMITH and its role in the AI Super Brain.
No. SMITH operates at the behavioral level — it adjusts confidence weights, routing rules, and quality thresholds. It generates improvement proposals that are reviewed by ARCHITECT before any structural changes are made.
Observable improvements typically appear within 1-3 evolution cycles (1-3 days). Routing accuracy improvements of 30-40% are common within the first week of operation on a new project domain.