Agentic AI, built for the enterprise
Autonomous agents are a new class of AI deployment — and they require a new class of engagement. Where generative AI is a content-production problem, agentic AI is an action-authorization problem. The model can now call tools, write to systems of record, send messages, and approve workflows. That capability is valuable, and it is the capability that creates the real risk surface.
Our agentic AI consulting engagements are designed for that reality. Every engagement ships a running agent on a scoped workflow, on a platform that the next agent and the agent after that will also run on, with the governance layer that lets a CISO and a compliance officer sign off without holding up the program.
What a pilot actually includes
A 6-week scoped pilot is the starting point for most client engagements. The deliverable is a running agent in production. The work breaks down roughly as follows:
- Weeks 1-2. Scope the workflow. Inventory the tools the agent will call. Map the approval gates. Document the data sensitivity and the audit requirements.
- Weeks 3-4. Stand up the platform — tool registry, sandboxed runtime, retrieval layer, observability. Build the agent against the first workflow.
- Weeks 5-6. Run in shadow mode; tune confidence thresholds and approval placements; cut over to production with the operational guardrails that matter.
After the pilot, subsequent agents ship in 1-3 weeks each because the platform components carry over. This compounds — by the end of a quarter a client running this model typically has four or five agents in production, each at a fraction of the incremental cost.
The agentic architecture we deploy
Every production engagement has six components:
- Reasoning model. Vendor-neutral — Claude, GPT, Gemini for cloud; Llama, Qwen, Mistral, Gemma for self-hosted. Choice is driven by data residency, latency, and per-workload evaluation.
- Tool layer. Scoped, typed, auditable. We use MCP (Model Context Protocol) as the canonical protocol so the tool catalog is portable across models and agents.
- Memory. Scratchpad for the current task; long-term retrieval (vector or relational) for accumulated context.
- Planner. Sometimes built into the model, sometimes orchestrated externally. Decomposes the goal into steps and decides the tool sequence.
- Guardrails. Input validation, content safety, PII redaction, policy enforcement. Runs both before and after the model call.
- Observability. Every input, every tool call, every output captured in a trace. The trace is the artifact that makes the system auditable.
The governance posture that makes this work
Clients deploying agentic AI without a governance framework end up halting the program after the first incident — not because the incident is catastrophic, but because they have no framework to decide how serious it is. Our engagements build the governance in from day one:
- Tiered approval gates (see our CISO framework insight). Autonomous, async-notification, inline-approval, multi-party-approval — each class of action mapped to the right tier.
- Full-trace audit. Append-only logs, retention matched to regulatory obligations, reviewable by compliance without engineer involvement.
- Evaluation pipeline. Production traces feed regression suites; drift triggers alerts; model or prompt changes are tested against history before deployment.
- Kill switch. A mechanism to disable an agent or a tool class in real time without waiting for code deploy.
Reference deployments
Our production agentic AI accelerators are the patterns we transfer into client deployments:
- TWSS CS Agent. CRM-agnostic AI case-resolution copilot for Salesforce, HubSpot, Dynamics, Zendesk, ServiceNow, Freshdesk, Gmail, and Outlook. 60% faster case response time, 100% audit coverage. See the retail-safety case study.
- TWSS AI Custom Agents. Enterprise agent platform with sandboxed runtime, multi-LLM routing, MCP tool protocol, and approval workflows. See the internal agent platform case study.
- TWSS Commercial Credit AI. Self-hosted 3-model ensemble (Qwen 2.5, Gemma 27B, Llama 3.3 70B) for commercial property lending. Zero external API calls, MBE/GSA-ready. See the commercial credit case study.
- TWSS eAlliance AI. Agentic automation for SAP S/4HANA, ECC, Oracle EBS, and Dynamics 365. See the SAP case study.
The full accelerators portfolio has 20+ production solutions across customer service, AP automation, commercial lending, SAP, staffing, and more.
Why Thoughtwave
Three reasons clients cite:
- Production AI portfolio. Patterns proven under real load, not whitepapers.
- Governance-first posture. We build for regulated environments by default; the audit and approval layer is not bolted on later.
- MBE and GSA-ready. Supplier-diversity credentials and GSA Schedule open procurement paths that pure-tech vendors cannot match.
For broader context, see the AI & Generative AI service and the accelerators portfolio. To start a scoped pilot conversation, book a consultation.