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 Choose the governance model that fits your operational risk profile
Deterministic Core
Linear DAG-orchestration for zero-drift, mathematically auditable execution.
Silicon Colosseum
Competitive multi-agent swarms utilizing stochastic persona mapping.
Neural Forager
Self-directed research loops governed by recursive belief states.
Director-Actor
Human-in-the-loop (HITL) reflexion for iterative creative refinement.
Deterministic Core · Stochastic Range · Multi-Agent Scale
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.
Read: Why "Chatbots" Fail →Stochastic Range
Intelligence is useless without constraints. We architect reasoning engines that explore solutions creatively, distilling output into strict deterministic execution graphs.
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.
Read: The 2026 Roadmap →Agentic Solutions
Real problems, strict constraints. Engineering autonomous systems built to survive and scale in production.
Clinical Oncology Agent
Navigates NCCN guidelines with toxicity-aware reasoning.
Deterministic orchestration of multi-drug regimens via Human-in-the-Loop (HITL) safeguards, enforcing strict physiological constraints and site-specific protocols.
View Case Study →[STRATEGIST] Compiling execution graph (DAG)...
[TOOL_CALL] Vision_Scanner: Extracting RECIST features.
[TOOL_CALL] Genomic_Service: Querying DPYD variants.
[WARN] DPYD deficiency (*2A) confirmed via tool output.
[AUDIT] Immutable trace committed to ledger.
[STATUS] PLAN_READY_FOR_EXECUTION
# --- STEP 1: PERCEPTION & NORMALIZATION ---
[PERCEIVE] Ingesting EMR: ER/PR+, HER2-, Echocardiogram.
[NORMALIZE] Mapping data to NCCN clinical vectors...
└─ Option A: AC-T (Doxorubicin) - 92% Efficacy
└─ Option B: TC (Docetaxel) - 88% Efficacy
# --- STEP 2: GOVERNANCE PRE-FLIGHT ---
[STRATEGIST] "Option A maximizes survival. Building node..."
[GOVERN] Intercepting node: prescribe(regimen="AC-T")
# --- STEP 3: POLICY-AS-CODE SHIELD ---
[DENY] 🛡️ POLICY_VIOLATION (rule: cardiotoxicity)
└─ Reason: "Patient LVEF is 48% (Threshold >50%)."
# --- STEP 4: DETERMINISTIC RECOVERY ---
[RE-PLAN] Reverting to Option B. Re-running policy check...
[PASS] Safety parameters cleared. Node approved.
# --- STEP 5: COMMIT & HANDOFF ---
[COMMIT] Immutable record written to the ledger.
[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-Agent Swarms and Multi-Armed Bandits.
View Case Study →Automated KYC & AML Screening Agent
SLM-first ReAct agent with deterministic risk scoring.
Single-threaded LangChain ReAct loop on CPU using 4-bit Qwen2.5-7B-Instruct. Pluggable BaseLLMBackend swaps to vLLM GPU inference via one-line config. Every tool call validated by Pydantic v2 with immutable PostgreSQL audit trails.
View on GitHub →# --- REACT TURN 1: TOOL_CALL ---
[REASON] Entity requires PEP & sanctions check.
[TOOL] sanctions_check(name="Acme Corp")
└─ Result: OFAC: CLEAR | EU: CLEAR | cache_miss
# --- REACT TURN 2: TOOL_CALL ---
[REASON] Fetching corporate registry for UBO extraction.
[TOOL] ubo_extract(doc="incorporation.pdf")
└─ Extracted: 3 beneficial owners → Neo4j graph
# --- REACT TURN 3: DETERMINISTIC ---
[TOOL] risk_score_combine() # pure algorithm, no LLM
[PASS] Composite Risk: 12/100 (Low). Cleared.
[AUDIT] Pydantic-validated KYCPacket → PostgreSQL.
[STATUS] ONBOARDING_APPROVED
Nash Marketing Agents
Neuro-symbolic ad auction simulator with Nash Equilibrium.
LLM proposes stochastic bidding strategies; symbolic Nash solver validates via iterative best-response with softmax annealing. VCG second-price engine enforces paid≤bid invariant. Monte Carlo 5000-sample win-probability estimation with multi-layer budget guardrails.
View on GitHub →# --- ROUND 1: STRATEGY PROPOSAL ---
[LLM] Bids: Agent-A=$2.40 B=$1.85 C=$3.10 D=$2.70
[SOLVER] Best-response iteration (softmax τ=0.3)...
# --- VCG AUCTION ENGINE ---
[AUCTION] Winner: Agent-C (bid $3.10)
└─ VCG payment: $2.70 (next-highest bid)
[ASSERT] paid≤bid invariant: 2.70 ≤ 3.10 ✓
# --- BUDGET GUARDRAILS ---
[WARN] Agent-A budget at 22% → soft warning
# --- CONVERGENCE @ ROUND 41 ---
[NASH] Equilibrium reached. Δu < 0.01
[TEST] 49/49 property-based tests passed.
[STATUS] SIMULATION_COMPLETE
Speculative Clinical GraphRAG
9-node LangGraph with hybrid retrieval & self-correction.
Qdrant vector store + Neo4j graph traversal with fusion scoring (α=0.7). Quad-track LLM backend with SemanticRouter auto-selection. Self-correcting feedback feeds violations + prior reasoning back via regenerate_with_feedback() with confidence decay. Deterministic escalation after max iterations — zero PHI persistence.
View on GitHub →# --- STEP 1: HYBRID RETRIEVAL ---
[QDRANT] Vector search: 12 ontology concepts
[NEO4J] Graph traversal: 34 taxonomic edges
└─ Fusion: α=0.7 vector + 0.3 graph
# --- STEP 2: DIFFERENTIAL ASSESSMENT ---
[LLM] Differentials: lung_ca(0.82) tb(0.11) sarcoid(0.07)
# --- STEP 3: SAFETY VERIFY ---
[FAIL] Neo4j edge lung_ca→metastasis missing
# --- STEP 4: SELF-CORRECTION (1/3) ---
[REGEN] Feeding violations + reasoning trace...
└─ Confidence: 0.82 → 0.72 (decay -0.1)
[PASS] Corrected path validated by all verifiers.
[AUDIT] Trace committed. Zero PHI persisted.
[STATUS] AWAITING_CLINICIAN_REVIEW
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 Agentic Orchestration
Hub-and-Spoke Pipelines for High-Stakes Industries
Deploy governed, multi-agent digital workforces engineered for mission-critical environments. We architect centrally orchestrated pipelines that execute complex, state-altering workflows under strict deterministic constraints and immutable audit ledgers.
# --- STEP 1: PERCEPTION & SIMULATION ---
[PERCEIVE] Ingesting patient genome (3.2B base pairs).
[TOOL_INVOKE] Cas9_Microservice -> Binding prediction...
└─ Candidate A: Efficiency 99.1% (Primary)
└─ Candidate B: Efficiency 84.5% (Fallback)
# --- STEP 2: DETERMINISTIC PLANNING ---
[PLANNER] Intent matched. Compiling execution DAG...
[GOVERN] Intercepting node: commit_design(candidate="A")
# --- STEP 3: POLICY-AS-CODE (OPA) PRE-FLIGHT ---
[DENY] 🛡️ POLICY_VIOLATION (rule: strict_off_target_toxicity)
└─ Critical Risk: "0.4% off-target match with 'TP53' (Tumor Suppressor)."
# --- STEP 4: DAG RE-COMPILATION ---
[RE-PLAN] Reverting to Candidate B. Re-running policy check...
[PASS] Zero critical off-target matches detected. Node approved.
# --- STEP 5: STATELESS EXECUTION & AUDIT ---
[EXECUTE] Irreversible write: finalize_design(candidate="B").
[AUDIT_LEDGER] Immutable trace cryptographically signed.
[STATUS] SAFE DESIGN LOCKED...
