Konstantin Sh.Data & AI Engineering
// 02. AUTONOMOUS INTELLIGENCE|AGENTIC WORKFLOWS, TOOL CALLING & PREDICTIVE ML

Agentic AI Solutions & Applied Machine Learning

Engineering production-grade Agentic AI architectures where intelligent LLMs execute structured tool-calling, interact directly with enterprise APIs and databases, automate complex multi-department routines, and operate alongside high-accuracy predictive Machine Learning models.

// HOMOLOGATED AGENTIC & ML ECOSYSTEM:
Agentic AI SystemsAutonomous AI AgentsPydantic AIAgent Development Kit (ADK)Tool-Calling & ActionsCatBoost / MLTime Series ForecastingMulti-Agent WorkflowsMLOps & EvalsRedis State CacheDocker ContainersPythonCloud Run & LambdaEnterprise API Connectors
// TYPICAL AGENTIC ARCHITECTURAL TOPOLOGY (TOOL-CALLING & WORKFLOW ORCHESTRATION)[ PROTOCOL: STRUCTURED JSON SCHEMA / ASYNC TOOL CALLING ]
+------------------------------------------------------------------------------------------------------+
| 01. TRIGGER & CONTEXT INGESTION (WEBHOOKS / SCHEDULED AGENTS / USER SESSIONS / EVENT STREAMS)        |
|  [ Operational Webhook Event ]    [ User Intent / Natural Language ]    [ Scheduled Anomaly Alert ]  |
+-----------------------------------|------------------------------------------------------------------+
                                    |
                                    v
+------------------------------------------------------------------------------------------------------+
| 02. AGENTIC REASONING & ORCHESTRATION LAYER (PYDANTIC AI / AGENT FRAMEWORKS / PYTHON RUNTIME)        |
|  - Strict system instructions & dynamic context injection (Retrieval & session state in Redis)       |
|  - Structured output schemas enforced via Pydantic models (Zero unparsed hallucination risk)         |
|  - Multi-agent coordination: router agents delegating tasks to specialized sub-agents                |
+-----------------------------------|------------------------------------------------------------------+
                                    |
                 +------------------+------------------+
                 |                                     |
                 v                                     v
+-------------------------------------+ +--------------------------------------------------------------+
| 03. STRUCTURED TOOL-CALLING RUNTIME | | 04. APPLIED MACHINE LEARNING INFERENCE                       |
|  [ Database Action Tools (SQL/DWH)] | |  [ CatBoost Demand & Pricing Models ]                        |
|  [ CRM / ERP / Accounting APIs ]    | |  [ Time-Series Forecasting Engines ]                         |
|  [ Outbound Notifications & Actions]| |  [ Anomaly Scores & Churn Probabilities ]                    |
+-------------------------------------+ +--------------------------------------------------------------+
                 |                                     |
                 +------------------+------------------+
                                    |
                                    v
+------------------------------------------------------------------------------------------------------+
| 05. EXECUTION, OBSERVABILITY & AUDIT LOGGING (MLOPS & EVALUATION PIPELINE)                           |
|  - Transactional rollback guarantees and idempotent action execution                                 |
|  - Latency tracking, token spend auditing, and regression evals (Continuous drift detection)         |
|  - Human-in-the-loop approval gates for critical financial/operational transactions                 |
+------------------------------------------------------------------------------------------------------+
//CAPABILITIES & ARCHITECTURES

Four Pillars of Agentic AI Solutions

Engineered to transform passive language models into deterministic, autonomous software that executes real-world operational workflows with measurable business ROI.

// 01. AGENTIC RUNTIMEPYDANTIC AI / ADK

Autonomous AI Agents & Tool Execution

Engineering deterministic AI agents utilizing Pydantic AI and modern agent frameworks. Unlike generic chatbots, these agents possess validated function-calling tools that query databases, invoke APIs, perform multi-step reasoning, and format outputs into strictly typed, machine-readable schemas.

>Strict type validation and schema constraints eliminating hallucinations
>Stateful agent execution with short/long-term memory stored in Redis
>Multi-agent delegation topologies (Supervisors and specialized Worker agents)
// 02. PREDICTIVE SCIENCECATBOOST / FORECASTING

Predictive Machine Learning & Forecasting

Development and production deployment of demand forecasting, time-series, dynamic pricing, and churn models utilizing CatBoost and LightGBM. We engineer high-accuracy models on structured tabular data that feed directly into operational decision engines.

>Feature engineering from historical transactional data and calendar seasonality
>Automated retraining pipelines triggered by data distribution shifts
>Low-latency model serving via Docker microservices on Cloud Run / AWS
// 03. ENTERPRISE INTEGRATIONSZERO MANUAL EFFORT

Enterprise Connectors & Automated Ops

Connecting agentic intelligence directly to corporate systems: PostgreSQL, BigQuery, Snowflake, Salesforce/HubSpot, ERP systems, and communication channels (Slack/Teams). Agents proactively detect operational discrepancies, execute reconciliations, and notify responsible stakeholders.

>Standardized tool connectors and Model Context Protocol (MCP) integrations
>Event-driven execution via webhooks and scheduled Prefect/Kestra workflows
>Elimination of manual operational copy-pasting and error-prone administrative tasks
// 04. MLOPS & GOVERNANCEPRODUCTION READY

MLOps, Continuous Evals & Cost Control

Comprehensive telemetry, automated testing, and regression evals for AI workflows. We track token consumption, response latency, and task completion accuracy, guaranteeing predictable cloud spend and preventing behavioral degradation during model updates.

>Automated regression evaluation suites assessing tool-call accuracy on edge cases
>Token caching, prompt compression, and strict budget caps per workflow
>Granular audit logging with step-by-step reasoning traceability
// ENGAGEMENT MODEL FOR AI INITIATIVES

Senior AI Engineering by Sprints & Hourly Allocation

Agentic AI and Machine Learning initiatives are executed directly with Konstantin Sh. under an auditable Time & Materials ($/hr) or sprint-based structure. No inflated agency markups or opaque deliverables: all prompt engineering, code, evaluation harnesses, and Docker containers belong 100% to your organization from day one.

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