Infosys AI Agent for Energy: Real-Time Multimodal Field Operations

Background / Overview​

Technology in play: graphene BCI meets agentic AI​

What INBRAIN brings: graphene‑based BCI‑Tx hardware​

• Graphene electrodes and films: INBRAIN’s implant technology relies on graphene-based micro‑contacts that are extremely thin and highly conductive. The company and several press accounts describe implants that are on the order of single‑digit to tens of micrometers in thickness (the company has stated device thicknesses and high contact densities in public materials). These material properties are central to their claim of ultra‑high signal resolution and low impedance for both recording and micrometric stimulation. Multiple technical summaries and partner releases confirm graphene’s electrical and mechanical advantages for flexible, high‑resolution neural interfaces.
• High channel count, bidirectional operation: INBRAIN’s platform is presented as a high‑density, bidirectional system—able to both decode neural signals at high spatial resolution and deliver targeted micrometric stimulation. Company material references contact counts and on‑device signal processing designed to enable closed‑loop control at clinically relevant latencies.
• On‑device processing and wireless controller: The platform includes a compact neural processor with wireless recharge and on‑implant telemetry to reduce latency and enable continuous monitoring outside the operating room.

What Microsoft brings: agentic AI, time‑series LLMs, and observability​

• Agentic AI and orchestration: Microsoft’s enterprise stack now emphasizes multi‑step, agentic workflows — systems that orchestrate tools, call APIs, and act autonomously on behalf of users or processes. The company’s agent frameworks and Azure AI Foundry tooling provide primitives for multi‑agent orchestration, telemetry, and traceability, which are explicitly designed to help enterprises govern autonomous systems. These features are especially relevant in regulated and safety‑critical domains such as healthcare where audit trails and observability are essential.
• Time‑series LLMs and continuous learning: Microsoft and partners have invested in models and tooling that handle long‑range time‑series data and can incorporate streaming signals into model state. Combining continuous neural telemetry with time‑aware models (sometimes described as time‑series LLMs) makes it technically feasible to build patient “digital twins” or embeddings that evolve with the patient and guide therapeutic decisions.
• Enterprise‑grade compute, compliance, and integration: Azure’s compliance, identity integrations, and data governance features are central selling points for life‑science customers. Microsoft provides integration patterns (Copilot Studio, Agent Frameworks, observability tooling) that can help teams move a prototype to a governed production deployment — a nontrivial requirement for clinical deployments.

Clinical and scientific potential​

How agentic AI could change neuromodulation​

• Move from open‑loop to adaptive closed‑loop therapies — rather than running fixed stimulation schedules, agents could monitor biomarkers and adjust parameters in real time to maximize benefit and minimize side effects.
• Accelerate biomarker discovery — continuous patient data streams fed into time‑aware models could surface micro‑patterns in neural dynamics associated with symptom relief or adverse responses, enabling more precise targeting.
• Personalize therapy across timescales — agents can adapt both within a session (milliseconds to minutes) and over longer horizons (days to months) to address disease progression, medication cycles, and behavioral state changes.

Early use cases that fit the technology stack​

• Parkinson’s disease — closed‑loop deep brain stimulation driven by detection of pathological oscillatory biomarkers is an established research priority; agentic systems could enable more nuanced adaptation to motor fluctuations.
• Epilepsy — seizure prediction and rapid on‑demand modulation has been explored for years; an autonomous agent that reliably detects pre‑ictal states and delivers targeted intervention could reduce seizure burden.
• Neuropsychiatric and memory disorders — more speculative but potentially transformative: continuous decoding of network dynamics could guide noninvasive or invasive modulation to ameliorate depressive episodes, intrusive memories, or cognitive deficits.

Regulatory, safety, and ethical hurdles​

Regulatory pathway and the Breakthrough Device context​

Safety concerns unique to autonomous closed‑loop neuromodulation​

• Action autonomy vs clinical oversight: Agentic systems that autonomously adjust stimulation parameters introduce a new failure mode: autonomous actioning that could, if mis‑specified, produce unintended neural states. Unlike document automation, these actions directly affect a human brain. Safety requires strict limits, multi‑tiered human‑in‑the‑loop checks (especially during early deployments), and fail‑safe mechanisms to revert to a safe nominal therapy if anomalies are detected.
• Model drift and distributional shift: Physiologic signals change over time. A learning agent could adapt in ways that appear beneficial short‑term but produce maladaptive long‑term changes absent proper constraints and monitoring.
• Verification and validation of learning agents: Traditional medical device validation methods assume fixed logic; self‑improving agents require new regulatory approaches for ongoing validation, versioning, and post‑market surveillance.

Ethics and consent​

• Informed consent for adaptive systems: Patients must understand not only that an implanted device will stimulate them but that the stimulation policy can change autonomously based on AI decisions. Consent forms, preoperative counseling, and ongoing patient control mechanisms will need to be more sophisticated.
• Agency and autonomy: Delegating therapeutic decision‑making to an AI raises questions about responsibility: who is accountable — the manufacturer, the cloud provider, the clinician, or the model author — when the agent takes an action that harms?
• Equity and bias: Models trained on limited or unrepresentative datasets may underperform in population subgroups, with ethical and clinical consequences.

Data governance, privacy, and security​

• Sensitive neural telemetry: Continuous brain signal streams are among the most sensitive health data types. Data governance must protect privacy, prevent unauthorized re‑identification, and implement strict access controls.
• Edge vs cloud tradeoffs: Sending raw neural telemetry to the cloud increases risk and latency; hybrid architectures that perform early processing on‑device and transmit aggregated/sanitized features can reduce exposure but complicate model training and updates.
• Supply chain and infrastructure risk: Reliance on third‑party cloud infrastructure for therapeutic decisioning introduces additional attack surfaces and vendor‑dependency risk. Robust contractual SLAs, encryption, identity verification, and secure observability will be essential.

Business, commercialization, and strategic implications​

Market and competitive position​

• Graphene‑based interface: improved signal fidelity and miniaturization potential compared with conventional metal electrodes.
• End‑to‑end BCI‑Tx platform: combining sensing, on‑device processing, stimulation, and AI decisioning.
• Strategic partnerships: clinical partnerships and capital to run trials and scale manufacturing.

The Microsoft partnership as strategic leverage​

• Compute and models at scale — Azure provides the infrastructure to host time‑series LLMs and heavy agent workloads.
• Agent tooling and governance — Microsoft’s agent frameworks and observability tooling reduce the operational burden of deploying and monitoring autonomous systems in enterprises.
• Commercial channels and enterprise credibility — aligning with a hyperscaler can ease procurement and enterprise adoption in health systems that already standardize on Azure.

Technical feasibility and near‑term realities​

What can realistically be achieved in the next 12–36 months?​

• Proof‑of‑concept integrations: Low‑risk pilots where Microsoft hosts analytics and non‑actionable clinician decision‑support agents using INBRAIN data — i.e., models that recommend but do not actuate.
• Human‑in‑the‑loop closed‑loop trials: Systems where agents propose parameter adjustments that clinicians review and approve before enactment.
• Edge‑assisted automation with heavy guardrails: Local inference for latency‑sensitive detection (on‑device) with cloud‑based agent coordination for higher‑level decisions and logging.

Engineering challenges to solve​

• Robust, low‑latency on‑device inference and secure model update pipelines.
• Continuous model validation and real‑time monitoring systems that detect drift, failure modes, and unsafe parameter changes.
• Reproducible, auditable agent decision logs tied to device actions for regulatory evidence.

Risks, unknowns, and unverifiable claims​

• Claims that agentic AI will imminently deliver organ therapeutics or fully autonomous brain‑intervention agents should be treated as aspirational. While the integration of continuous neural telemetry and time‑aware models is promising, translating those capabilities into safe, autonomous clinical therapy will require substantial evidence and new regulatory frameworks.
• Any public numbers about total funding, channel counts, or device thickness should be cross‑checked against up‑to‑date company filings and credible press reports — these figures have been updated several times in recent years as INBRAIN’s financing evolved.
• The long‑term neural effects of repeatedly applying AI‑driven modulation patterns are not yet known; chronic studies, patient registries, and transparent post‑market surveillance will be necessary.

• Descriptive analytics / clinician decision support — low regulatory risk, high near‑term feasibility.
• Semi‑autonomous, clinician‑supervised closed‑loop — moderate risk, requires robust monitoring and regulatory engagement.
• Fully autonomous, self‑optimizing therapeutic agents — high regulatory and ethical risk; long timeline.

Practical guidance for clinicians, researchers, and IT leaders​

• Treat agentic BCI deployments as systems engineering projects, not point products. They require integrated work across neurosurgery, clinical engineering, cloud operations, and AI governance.
• Prioritize architectures that allow rapid rollback, human override, and transparent logs of agent decisions.
• Demand provenance and model cards for any AI models that recommend or initiate therapy adjustments.
• Require explicit contractual obligations from cloud partners concerning data residency, incident response, and model update traceability.

What to watch next​

• Pilot design announcements — look for details on whether early pilots are limited to analytics or include automated actuation.
• Regulatory filings and trial protocols — the submission of IDEs or protocol descriptions will indicate the level of autonomy proposed for evaluation.
• Technical disclosures — publications or preprints describing the time‑series LLMs, model architectures, or closed‑loop control strategies will signal seriousness and reproducibility.
• Third‑party audits and ethics oversight — independent safety audits and published ethics review outcomes will increase confidence in agentic deployments.

Conclusion​