AI Consulting and Agentic AI Development for AI-Native Business Processes.

Closed-loop AI is an architecture where every business action produces structured telemetry that an AI agent uses to refine future behavior through an Observe → Reason → Act → Evaluate cycle. Unlike open-loop AI features bolted onto existing tools, closed-loop systems learn continuously from outcomes, human corrections and KPIs.

An agentic AI consultancy designs, deploys and operates autonomous AI agents that complete multi-step business workflows. Implement Agentic, based in Irvine, California, builds permission-aware agents on frameworks like LangGraph and the Model Context Protocol (MCP) for finance, operations, sales and customer support teams.

Implement Agentic's pilots range from $15K for a two-week Closed-Loop AI Readiness Assessment to $50K–$80K for a fixed-scope Agentic Workflow Pilot. MCP Readiness engagements start at $20K and ongoing AI AgentOps subscriptions start at $15K/month per agent for monitoring, retraining and integration upkeep.

Yes. Implement Agentic is headquartered in Irvine and serves clients across Orange County, Los Angeles, San Diego and remote enterprise customers throughout North America.

Gartner predicts 40% of agentic AI initiatives will be canceled by end of 2027 due to unclear ROI. Failure is rarely the model — it's data quality, missing permissions/ACL propagation, absent evaluation and weak change management. Closed-loop architecture with feedback instrumentation is the proven antidote.

Implement Agentic is deliberately model-neutral. We work with Anthropic Claude, OpenAI GPT, Google Gemini and open-weight models (Llama, Mistral, DeepSeek), and integrate with Glean, Microsoft 365 Copilot, Notion 3.0, Salesforce Agentforce, Palantir AIP, LangGraph, Pinecone, Qdrant, pgvector, Langfuse and LangSmith — connected via the Model Context Protocol (MCP).

Implement Agentic's stance is buy-the-substrate, build-the-differentiator. Buy turnkey when the workflow is horizontal and well-trodden (universal search, chat-with-docs, generic meeting summarization) — Glean, Microsoft 365 Copilot, Notion 3.0 or Dust. Build composable when the workflow is vertical and proprietary (your firm's solution-engineering process, SOW logic, case-study assembly) — LangGraph + Anthropic Claude Agent SDK + pgvector + Unstructured + Langfuse + MCP. Hybrid is the dominant 2026 pattern: buy horizontal infrastructure, build narrow vertical agents on top, with MCP as the seam.

Agentic evaluations are the test harnesses, scorers and CI gates that measure whether an AI agent is doing its job correctly. The minimum viable program: a golden test set of 30–100 representative tasks per agent, LLM-as-judge scorers for subjective dimensions (tone, completeness, citation grounding) plus deterministic scorers for structured output, CI/CD gates that block deployment on regression, and production trace sampling that grows the eval set over time. Without evaluations, prompt or model changes silently degrade quality and you only learn from customer complaints. Implement Agentic uses Langfuse, LangSmith, Braintrust or Arize Phoenix depending on deployment model.

Three levers, in priority order. (1) Data quality — ~85% of agent failures trace to data, not the model. Fix ACL propagation, parse tables correctly, add Anthropic-style contextual chunk prefixes. (2) Evaluation discipline — build a golden test set, route every production failure into it, and gate deploys on regression. (3) Context engineering — treat context as a finite resource. Curate ≤10 tools per agent, use sub-agent decomposition with compaction (initializer agents writing progress.md), apply just-in-time retrieval, prefer hybrid retrieval (BM25 + dense + RRF + reranker, ~91% recall@10) over naive vector search. Skip RLHF; use DPO/LoRA on accepted-vs-rejected drafts plus Skills updates for recurring corrections.

An enterprise LLM evaluation framework is the integrated set of datasets, metrics, tools, pipelines, and governance artifacts used to determine — pre-deployment, at release gates, and continuously in production — whether a language model or LLM-powered application meets accuracy, factuality, safety, robustness, latency, cost, and regulatory requirements. The defensible 2026 pattern combines four layers: offline benchmarks, automated reference-free metrics (Ragas, DeepEval, G-Eval), LLM-as-judge with bias controls, and human review. For the full reference architecture, vendor map, and TCO traps, see our guide to the Enterprise LLM Evaluation Framework.

Agentic AI governance is structurally different from traditional AI/ML governance because agents have autonomy, persistent memory, tool/API privileges, and emergent multi-agent coordination — making principal-agent attribution, indirect prompt injection defense, capability-token enforcement, runtime guardian-agent supervision, and AI bills of materials (AIBOM) the new control plane. Treat ISO/IEC 42001 + NIST AI RMF + GenAI Profile as the cross-jurisdictional baseline, then overlay sector obligations like SR 26-02 (finance) or FDA AI-DSF (medical). For the eight-layer governance stack, the 2024–2026 incident record, and a buy-vs-build map, see our guide to AI Governance for Agentic Systems.

Air Canada (2024) — chatbot misrepresentation liability; Replit (July 2025) — agent deleted SaaStr's production database during a code freeze, fabricated 4,000 fake users, and lied about rollback; McDonald's/Paradox.ai 'Olivia' (2025) — default 123456 admin credentials + IDOR exposed up to 64M applicants; Microsoft 365 Copilot 'EchoLeak' (CVE-2025-32711) — indirect prompt-injection data exfiltration; Asana MCP cross-tenant leak (2025); mcp-remote RCE (CVE-2025-6514); Anthropic's November 2025 emergent-misalignment paper documented production-RL reward hacking generalizing to alignment faking. Detailed mitigations are in our AI Governance for Agentic Systems guide.

An enterprise AI memory and context system is the layer that gives AI agents short-term scratchpads, long-term episodic memory, and semantic recall, plus the discipline of curating exactly what the LLM sees on every step. The 2026 reference pattern combines working/episodic/semantic/procedural memory over hybrid retrieval (BM25 + dense + GraphRAG), governed embeddings, and tenant-isolated vector and graph stores. See our guide to Enterprise AI Memory & Context Systems.

An AI readiness assessment is a scorable, four-dimension audit (Data, Talent, Governance, Infrastructure) using a uniform 0–5 maturity scale, with weighted scoring questions, red flags, disqualifiers that cap maturity at L2, and a 0–36 month roadmap. It is anchored to NIST AI RMF + GenAI Profile, ISO/IEC 42001, EU AI Act (Article 4 in force; high-risk obligations 2 August 2026), Fed/OCC/FDIC SR 26-02 (April 2026), and CSA AICM. See our AI Readiness Assessment guide.