Arash Nicoomanesh
Agentic AI Architect
AI Engineering Services & Consulting
Beyond the Hype of Expensive Chatbots
Bridging Strategic Business Intent with Adaptive Agentic Systems
Chatbots and prompting are parlor tricks; systems engineering is a discipline. While standard automation is fragile and raw LLMs are unpredictable, we build for Architectural Resilience. By separating stochastic reasoning from deterministic execution, we deliver multi-agent systems that plan complex workflows, reason through ambiguity, and learn from outcomes. The result is autonomous execution backed by absolute reliability, strict governance, and predictable ROI.
The Black Box
Probabilistic WrappersStandard chatbots and hardcoded workflows are fragile cost centers. They create technical debt and require constant human supervision to handle edge cases.
> User: "Process invoice #4492"
> Agent: Guessing API payload...
> FATAL: Max recursion depth reached.
> Hallucinated parameter: 'amount_null'
> - Unbounded Action Space
- Probabilistic Guessing
- Zero Blast-Radius Containment
The Glass Box
Deterministic State MachinesTrue value requires moving from automated processes to autonomous reasoning, backed by strict physical boundaries and mathematically auditable execution.
> Orchestrator: DAG Received.
> Shield: OPA Policy Check [PASS]
> Worker: Executing Step 1 (Idempotent)
> HTTP 200: Execution Committed.
> Audit Hash: 0x8f92a4...
> - Constrained Execution Graphs
- Policy-as-Code Guards (OPA)
- Immutable Audit Trails
Architecture Philosophy Real Agentic Systems Reason and Learn within intelligent Architecture
Generative AI builds prototypes; governed state machines build platforms. We engineer neuro-symbolic architectures that cross the Planning Rubicon—isolating probabilistic reasoning from deterministic execution to deploy fault-tolerant, mathematically auditable digital workforces.
Deterministic Core
Enterprise autonomy requires absolute reliability. We treat the LLM as an untrusted guest within a rigid state machine, ensuring critical actions are governed by deterministic logic, not probabilistic guesses.
Read: Why "Chatbots" Fail →Stochastic Range
Intelligence is useless without constraints. We architect reasoning engines that explore solutions creatively, distilling their cognitive output into strict deterministic execution graphs (DAGs) to prevent hallucinations in high-stakes environments.
Read: The Engine of Reasoning →Multi-Agent Scale
A single agent is a prototype; centralized orchestration is a platform. We scale isolated bots into parallel, DAG-driven sub-agents managed by a central orchestrator, ensuring fault-tolerant, durable execution.
Read: The 2026 Roadmap →
Agentic Solutions
Real problems, strict constraints. Engineering autonomous systems built to survive and scale in production.
Medical Triage Agent
Reasons like a clinician; prioritizes care safely.
> [INIT] Agentic_Loop_v2.4
> [DATA] EHR Patient_ID: 99x-72 linked
> [THINK] Analyzing Vitals: BP 160/100, Temp 101F
> [REASON] Cross-ref: Type 2 Diabetes history
> [RISK] Sepsis Probability: 72% (Critical)
> [PLAN] Generating Priority_Alpha alert...
> Waiting for clinician auth...
Clinical Oncology Agent
Navigates NCCN guidelines with toxicity-aware reasoning.
Autonomously optimizes multi-drug regimens via Human-in-the-Loop (HITL) orchestration, enforcing strict physiological toxicity constraints and site-specific protocols.
View Case Study →[MANAGER] Spawning sub-agent workers...
[WORKER-1] Radiology_Bot: Analyzing CT scans.
[WORKER-2] Pharmacogenomics_Bot: Querying variants.
[ALERT] DPYD deficiency (*2A) detected.
[COMMIT] State pushed to Temporal store.
[STATUS] CONSENSUS_REACHED
# --- STEP 1: OBSERVATION & SIMULATION ---
[OBSERVE] Ingesting EMR: ER/PR+, HER2-, Echocardiogram.
[SIMULATE] Running NCCN guideline predictions...
└─ Regimen A: AC-T (Doxorubicin) - 92% Efficacy
└─ Regimen B: TC (Docetaxel) - 88% Efficacy
# --- STEP 2: REASONING & INTENT ---
[THINK] "Regimen A maximizes survival. Formulating orders."
[ACTION_INTENT] draft_prescription(regimen="AC-T")
# --- STEP 3: POLICY GOVERNANCE SHIELD ---
[POLICY_INTERCEPT] 🛡️ DENY (rule: cardiotoxicity)
└─ Critical Risk: "Patient LVEF is 48% (Threshold >50%)."
# --- STEP 4: RECOVERY & RE-GROUNDING ---
[PLAN_UPDATE] Dropping Regimen A. Proposing Regimen B.
[PASS] Clears all physiological safety parameters.
# --- STEP 5: DETERMINISTIC EXECUTION ---
[SYSTEM_COMMIT] Irreversible write: append_emr_draft.
[STATUS] AWAITING ONCOLOGIST SIGN-OFF...
Molecular Discovery
High-throughput docking agent that halts synthesis of hepatotoxic structures.
View Case Study →Biomedical Hypotheses
Hypothesis-driven retrieval over PubMed for repurposing leads.
View Case Study →Marketing ROI Optimizer
Continuously reallocates budget using Multi-armed bandits.
View Case Study →Consulting Services
Build and Operate Intelligent Systems that Last
The Blueprint
AI Strategy & Advisory
De-risk your AI investment with a comprehensive technical architecture, cost model, and execution strategy
The Forge
Custom Agentic Prototype
Transform your validated blueprint into a production-grade agentic prototype. We engineer the deterministic skeleton, stateful control loops, and pre-commit verification gates required to cross the Planning-Rubicon.
The Nexus
Agentic Deployment & Scale
Transition your prototype into a fault-tolerant agentic system. We implement Policy-as-Code guardrails, temporal state management, and token-level AgentOps for zero-trust production autonomy.
Generative AI & LLM Engineering
Determinism, Neuro-Symbolic Logic and Optimization
Moving beyond fragile API wrappers, we engineer robust, high-stakes LLM infrastructure built for survival in enterprise production environments. We bridge the critical gap between probabilistic text generation and strict neuro-symbolic logic, ensuring your AI systems execute with absolute predictability.
Enterprise Swarm
Governed Multi-Agent Orchestration at Scale
Deploy governed, multi-agent digital workforces engineered specifically for high-stakes industries. We build specialized, autonomous swarms capable of executing complex, long-running workflows while operating under the strictest enterprise constraints.
# --- STEP 1: OBSERVATION & SIMULATION ---
[OBSERVE] Ingesting patient genome (3.2B base pairs).
[SIMULATE] Running Cas9 binding prediction...
└─ Candidate A: Efficiency 99.1% (Primary)
└─ Candidate B: Efficiency 84.5% (Fallback)
# --- STEP 2: REASONING & INTENT ---
[THINK] "Candidate A maximizes therapeutic edit rate. Formulating payload."
[ACTION_INTENT] commit_design(candidate="A")
# --- STEP 3: POLICY GOVERNANCE SHIELD ---
[POLICY_INTERCEPT] 🛡️ DENY (rule: strict_off_target_toxicity)
└─ Critical Risk: "0.4% off-target match with 'TP53' (Tumor Suppressor)."
# --- STEP 4: RECOVERY & RE-GROUNDING ---
[PLAN_UPDATE] Dropping Candidate A. Proposing Candidate B.
[POLICY_EVAL] Re-verifying Candidate B against security constraints...
[PASS] Zero critical off-target matches detected.
# --- STEP 5: DETERMINISTIC EXECUTION ---
[SYSTEM_COMMIT] Irreversible write: finalize_design(candidate="B").
[STATUS] SAFE DESIGN LOCKED...
Knowledge Base
Foundation Theory Speeds Iteration. Understand Once, Move Faster Forever.
Fine-Tuning DeepSeek R1 on Medical Chain-of-Thought
Latest technical walk-through on enhancing medical-reasoning LLMs with CoT fine-tuning
Gemma 3n Edge AI for Support Bots
Low-memory, high-speed training on customer-support data
A Dive Into LLM Output Configuration, Prompt Engineering Techniques and Guardrails
Master temperature, top-p, and frequency penalties. Learn advanced prompt engineering techniques and implement robust guardrails to ensure reliable, deterministic, and safe LLM outputs in production environments.
Few-Shot and Zero-Shot Learning : Unlocking Cross-Domain Generalization
Push LLMs beyond narrow fine-tuning. Discover how cross-domain generalization works, and learn to leverage in-context learning, prompt templates, and semantic anchors to achieve high accuracy on unseen tasks without retraining.
Fine-Tune Gemma-3 12B with Unsloth
End-to-end Unsloth & TRL workflow for customer service
Beyond the Hype of Expensive Chatbots
Move past brittle, prompt-reactive bots toward Architectural Resilience. This deep dive explores crossing the 'Planning Rubicon' by separating the Deterministic Core from the Stochastic Range. We analyze the three gates of agency—Commitment, Grounding, and Execution—to distinguish true autonomous agents from sophisticated token-simulators. By implementing internal verification machinery and epistemic tethers, we transform stochastic reasoning into irreversible causal action that drives predictable enterprise ROI.
The Planning-Rubicon: Why the Vast Majority of AI Agents Are Just Expensive Chatbots
Beyond the Wrapper: Why 2026 Will Separate Agent Infrastructure from Agent TheaterMost of today's systems aren't agents—they are expensive chat loops. True autonomy requires crossing an architectural threshold defined by commitment, grounding, and temporal awareness, where an LLM's text becomes verifiable, irreversible action.
