Days 1–5 - Agentic AI workshop.
Your team and ours map a single operational workflow. We design the agent architecture, the governance envelope, and the eval criteria. You leave with a reference architecture and a build plan.
Agentic AI Development Services for the Operations Energy Companies Actually Run
Field. Grid. Plant. Trading floor. Build agentic systems that act on telemetry - not chat interfaces that hand it back to a human.
Most energy and utility operations today still look like this, even after a decade of digital transformation:
Every model is honest about what it does. None of them act. The system stays linear: signal → human → another system. Human latency is the bottleneck, and humans are the resource you can't hire faster.
An agentic system reframes the picture. The agent watches the telemetry, reasons across the same data the operator does, and acts - within governance limits the operator sets.
In a transmission utility, that means an agent that detects a developing fault pattern, opens a work order in the CMMS, attaches the relevant inspection history, and dispatches a crew. The human approves. The agent did the typing.
In a generation portfolio, that means an agent that watches forecast deviation, recommends a dispatch change, and books it once the trader signs off. The trader makes the decision. The agent stages it.
In an oilfield, that means an agent that correlates a pressure anomaly to a known failure mode, drafts the diagnostic and the remediation steps, and queues the truck roll. The supervisor approves. The agent compresses the response.
The pattern is the same: agentic AI development services convert linear, human-paced operations into agent-paced operations with human governance. That's the after picture worth building toward.
A reasoning layer. Multi-step planning, tool use, and self-correction - not a single inference call.
A safety and governance layer. Action budgets, escalation thresholds, audit logs, human-in-the-loop checkpoints, and reversibility.
A telemetry and feedback layer. Eval suites, drift monitoring, and learning loops so agents improve under your operating conditions, not benchmarks from a paper.
Agent architecture and orchestration - multi-agent system design, tool registries, memory and state management.
Reasoning engine selection - LLM-class model selection (open-weight or hosted), function-calling, planning frameworks, retrieval design.
Energy-systems integration - SCADA, historians, CMMS, OMS/ADMS, EAM, ETRM, and the protocol translators that connect them.
Safety, governance, and eval - action policy frameworks, eval harnesses tuned to energy use cases, drift and risk monitoring, governance reporting.
MLOps for agents - versioning, replay, canary deployment, and incident response patterns built for autonomous workflows.
These are the patterns we see going to production in 2026:
Autonomous fault detection and dispatch. Agents that watch grid telemetry, detect developing faults, generate work orders, and stage crew dispatch.
Generation forecast and dispatch assistance. Agents that watch forecast versus actuals and recommend re-dispatch within governance limits.
Compliance and regulatory reporting. Agents that draft NERC-CIP, FERC, or state-level reports from operational data with human review.
Field maintenance assistants. Multi-modal agents that triage field reports, query historical incidents, and draft remediation steps.
Trading desk assistants. Agents that monitor market data, internal positions, and risk limits - staging trades and flagging anomalies for the human trader.
Outage management. Agents that correlate customer reports, AMI data, and weather signals to prioritize restoration.
We don't open with a 12-month build. We open with a 5-day workshop because energy buyers - rightly - won't commit capital to autonomous systems they haven't stress-tested.
Your team and ours map a single operational workflow. We design the agent architecture, the governance envelope, and the eval criteria. You leave with a reference architecture and a build plan.
We build the agent and run it in shadow against live telemetry. It proposes actions; humans execute. We measure agreement, false positives, and latency.
Agent takes consequential action under human approval. Governance, audit, and incident response patterns get tested under real load.
As the eval and governance posture earns trust, the agent's action budget expands. New workflows join the platform. The platform itself becomes the moat.
Most agentic AI vendors come from a chat-app background. Logiciel's energy practice comes from operations. Three things show up in the work:
We model agents as control systems, not chatbots. Action budgets, reversibility, latency targets, and safety envelopes are first-class. Conversation is incidental.
We integrate with the systems your operators already trust. CMMS, OMS, ADMS, historians, ETRMs - not just a vector database and a chat UI.
We treat eval as engineering. Every agentic workflow ships with an eval harness tuned to the operational outcome, not an LLM benchmark.
Agentic AI development services are engineering engagements that build autonomous, reasoning AI systems - not single-shot models or chat interfaces. An agentic system can plan, use tools, take action against external systems, and self-correct under defined governance. For energy operators, that typically means agents that integrate with SCADA, historians, CMMS, OMS, and ETRMs to act on operational telemetry inside a governed envelope.
A traditional ML model produces a prediction. A human reads it and decides what to do. An agentic system reasons across multiple inputs, plans a sequence of steps, calls tools or APIs, and takes action. Crucially, an agentic system is built with explicit safety, governance, and reversibility patterns so the autonomy is bounded.
Every agentic workflow Logiciel builds includes an action policy framework (what the agent can and cannot do), human-in-the-loop checkpoints on consequential actions, an action budget per time window, full audit logging, reversibility patterns, and an evaluation harness that runs continuously against operational data. We start every engagement in shadow mode so the agent's behavior is measured before it acts.
Workflows with three traits: high volume of telemetry that humans can't process in real time; well-understood action patterns that already have human playbooks; and clear governance boundaries. That maps to fault detection and dispatch, outage management, maintenance triage, compliance reporting, and forecast-versus-actuals re-dispatch. Workflows with poor governance maturity are not good first candidates.
A 5-day discovery workshop is a defined fixed-price engagement. A first-agent build in shadow mode typically runs as a 10 to 12 week engagement. Long-term agentic platforms run on a dedicated squad model. We scope and price after the workshop because the cost is sensitive to integration depth (SCADA, CMMS, OMS connectivity) more than to the AI itself.
Yes. Most energy agentic AI deployments are hybrid - reasoning runs in a cloud or DMZ environment, while integration with OT systems happens through patterns approved by your security architecture. We work inside the boundaries your CIP and ICS security teams set, not around them.
Yes. Many of our energy engagements end up as multi-agent ai systems where specialized agents (telemetry watcher, planner, executor, auditor) collaborate under an orchestration layer. We design these patterns explicitly - they don't emerge by accident.
Pick one operational workflow. Five days, joint team, no procurement overhead. You leave with a reference architecture for a production-grade agentic AI system inside your environment - and a build plan you can approve without another vendor cycle.