National security enterprise leaders are currently caught in a technical debt dilemma: a $100M+ trap where the mandate for AI dominance is clear, but the cost and operational downtime of “ripping and replacing” legacy software and data lakes is prohibitive. Helix solves this dilemma by allowing users to bring AI to the data and existing software, rather than demanding the data move to a proprietary cloud, enabling agencies to preserve 100% of their current IT ROI while gaining immediate agentic capability, at scale.

Helix works with existing software and hardware, and operates in classified environments as well as NIPR (Non-Secure Internet Protocol Router). It is at Technology Readiness Level 7 and is already deployed within intelligence organizations, where it is actively saving thousands of man-hours, reducing operational risk, and accelerating time to decision. Unlike rigid systems for others in the government software space, Helix adapts instantly. As a part of scalable data solutions, it accesses, analyzes, and acts on petabytes (PB) of information in seconds, not days or weeks. Tasks that once took a team a month can now be completed by an individual in hours.

“Trust is earned in denied environments, not in labs,” said Joe Logue, CEO of Two Six Technologies. “Helix is not aspirational R&D; it is a proven, reliable operational product already deployed at two government agencies in classified environments, where it supports critical national security missions today. It was designed from day one to meet the most rigorous security and compliance standards of the national security community. Two Six’s ‘blueprint’ is to combine first-hand mission expertise with cutting-edge technology and operational deployment at speeds no other providers can achieve. Helix is no exception.”

Early adopter IC and DoW partners chose Helix for its ability to retain control of their data in their own trusted environment with a model-agnostic, industry protocol standard foundation that integrates seamlessly across classified, legacy, and modern systems without forcing infrastructure changes or requiring them to convert to a proprietary ontology.

Helix acts as a “brain” or orchestrator that integrates directly into existing workflows. It allows a single user, in role-based logic, to task AI agents to engage with existing data, tools, and software applications in a fully secure and compliant environment. By accelerating decision cycles and eliminating manual data stitching, Helix empowers defense and intelligence teams to act faster and with greater confidence in high-stakes environments where failure is not an option, according to the company.

Source: Two Six Technologies

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This report is a comprehensive guide to understanding and mitigating the unique risks associated with the rise of agentic artificial intelligence (AI) within critical infrastructure, including the defense sector. The CSI highlights general security considerations for agentic AI, including the inherited risks of large language models (LLMs), increased attack surfaces, increased complexity, the evolving security landscape as the technology matures, and the need to address AI security as part of established cybersecurity paradigms.

Unlike traditional generative AI, which typically requires human validation, agentic AI systems are designed to operate autonomously, making them a powerful tool. This presents both unprecedented opportunities and significant cybersecurity challenges organizations must address to protect national security and critical infrastructure.

“Careful Adoption of Agentic AI Services” outlines risk spaces to consider, including:

•    Privilege Risks: Over-privileged agents can amplify the impact of a single compromise.
•    Design and Configuration Risks: Insecure design and provisioning can introduce vulnerabilities.
•    Behavior Risks: Goal misalignment, specification gaming, deceptive behavior, and emergent capabilities can lead to unexpected or undesirable outcomes.
•    Structural Risks: The interconnected nature of agentic systems increases the attack surface and complexity.
•    Accountability Risks: The opacity of agentic systems makes accountability hard to trace, complicating auditing and compliance.

Securing agentic AI systems requires proactive measures that address risks introduced by autonomy, interconnected components, and evolving capabilities. The report recommends deploying agentic AI incrementally, continuously assessing against evolving threat models, and maintaining strong governance, explicit accountability, rigorous monitoring, and human oversight which are essential for safe and secure operation.

Organizations that use agentic AI services, including those in the defense sector, are encouraged to review this guidance and adopt the outlined cybersecurity mitigations.

Other agencies co-sealing this CSI are the Canadian Centre for Cyber Security (Cyber Centre), the U.S. Cybersecurity and Infrastructure Security Agency (CISA), the New Zealand National Cyber Security Centre (NCSC-NZ), and the United Kingdom National Cyber Security Centre (NCSC-UK).

Read the full report here.

Source: NSA

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SeekrFlow is a complete end-to-end AI operating system that unifies model hosting, fine-tuning, agent orchestration, and full agent observability in a single platform purpose-built for the most demanding environments, including air-gapped, disconnected, and tactical edge settings. Deployed across the U.S. Army, U.S. Navy, and other defense agencies, and awardable through the CDAO Tradewinds Solutions Marketplace, Seekr has established itself as a trusted AI provider for mission-critical government operations. Unlike fragmented solutions that require stitching together multiple tools, SeekrFlow Agents give organizations a secure, specialized, and fully deployable solution on-premises and in the cloud, enabling faster decision-making and reducing the time and overhead required to operationalize AI at scale.

“Our collaboration with GDIT brings secure, transparent, and mission-ready AI to the heart of government operations,” said Rob Clark, president of Seekr. “By combining Seekr’s agentic AI with GDIT’s proven leadership in federal mission delivery, we’re enabling agencies to move faster, operate smarter, and achieve outcomes once thought impossible.”

“Federal agencies need cutting-edge emerging technology capabilities to meet the pace and complexity of today’s missions,” said Ben Gianni, GDIT senior vice president and chief technology officer. “Our collaboration with Seekr will enable us to deliver differentiated, agentic AI solutions that enable our customers to advance missions faster, smarter and more securely.”

Source: Seekr

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Security pipelines at global enterprises and federal agencies now generate trillions of events. Logs, telemetry and alerts outpace human triage and fuel blind spots, burnout and slower responses as uncertainties evolve by the minute. To counter this, Leidos, along with VAST and NVIDIA, brings accelerated detection and an AI-ready data foundation together – NVIDIA Morpheus and NVIDIA BlueField Data Processing Units(DPUs) for real-time inspection and inference, paired with VAST DataEngine and VAST DataBase to keep years of telemetry instantly searchable for analysts and AI alike. The result is less noise and faster, policy-aware action that helps teams move from alert fatigue to confident, automated security.

“At GTC DC, we’re showing what happens when data, models and agents operate as one system,” said Randy Hayes, vice president, public sector at VAST Data. “With Leidos’ mission expertise, NVIDIA AI architecture and the VAST AI Operating System unifying data and orchestration, security teams can analyze years of telemetry instantly, surface hidden signals with vector search and let AI agents execute policy-driven responses – simplifying operations, accelerating outcomes and improving critical system security.”

“Federal missions demand cyber platforms that scale, adapt and reduce analyst burden,” said Josh Salmanson, vice president and cybersecurity practice lead at Leidos. “By pairing Leidos’ cyber tradecraft with the NVIDIA AI stack and the VAST AI Operating System, we’re eliminating slow, manual steps that stall response. The results are agentic workflows that correlate evidence, construct timelines and recommend or execute actions with the speed and rigor mission environments require.”

Source: VAST Data Federal

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Enterprises moving AI agents from pilot to production still face challenges with speed, cost, and reliability, especially in mission-critical sectors like healthcare, finance, government, retail, and manufacturing. This partnership combines Groq’s inference speed, cost efficiency, and access to the latest open-source models with IBM’s agentic AI orchestration to deliver the infrastructure needed to help enterprises scale.

Powered by its custom LPU, GroqCloud delivers over 5X faster and more cost-efficient inference than traditional GPU systems. The result is consistently low latency and dependable performance, even as workloads scale globally. This is especially powerful for agentic AI in regulated industries.

For example, IBM’s healthcare clients receive thousands of complex patient questions simultaneously. With Groq, IBM’s AI agents can analyze information in real-time and deliver accurate answers immediately to enhance customer experiences and allow organizations to make faster, smarter decisions.

This technology is also being applied in non-regulated industries. IBM clients across retail and consumer packaged goods are using Groq for HR agents to help enhance automation of HR processes and increase employee productivity.

“Many large enterprise organizations have a range of options with AI inferencing when they’re experimenting, but when they want to go into production, they must ensure complex workflows can be deployed successfully to ensure high-quality experiences,” said Rob Thomas, SVP, software and chief commercial officer at IBM. “Our partnership with Groq underscores IBM’s commitment to providing clients with the most advanced technologies to achieve AI deployment and drive business value.”

“With Groq’s speed and IBM’s enterprise expertise, we’re making agentic AI real for business. Together, we’re enabling organizations to unlock the full potential of AI-driven responses with the performance needed to scale,” said Jonathan Ross, CEO and founder at Groq. “Beyond speed and resilience, this partnership is about transforming how enterprises work with AI, moving from experimentation to enterprise-wide adoption with confidence, and opening the door to new patterns where AI can act instantly and learn continuously.”

The partnership also plans to integrate and enhance Red Hat open source vLLM technology with Groq’s LPU architecture to offer different approaches to common AI challenges developers face during inference. The solution is expected to enable watsonx to leverage capabilities in a familiar way and let customers stay in their preferred tools while accelerating inference with GroqCloud. This integration will address key AI developer needs, including inference orchestration, load balancing, and hardware acceleration, ultimately streamlining the inference process.

Source: IBM

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“Intel Agents for the physical world transform real-time event detection into AI-powered real-time event, threat, and risk intelligence—a fundamental leap forward for the category Dataminr first pioneered,” said Ted Bailey, founder and CEO of Dataminr. “We can now tell our clients not just the ‘what,’ but also the “so what,” for everything Dataminr discovers across the physical, digital, and cyber domains. Our customers essentially have a fleet of AI agents working for them 24/7, autonomously asking and answering critical questions to provide the context customers need to respond with speed and confidence.”

With Intel Agents applied to physical events, customers gain immediate context that provides the full picture for any event, at any given moment, in real time. Powered by Dataminr’s domain-specialized LLMs for real-time reasoning, Intel Agents continuously ask and answer hundreds of questions about every event, threat, and risk as they unfold. To accomplish this, a fleet of AI agents scours the billions of data signals ingested daily from more than 1M public data sources, searches across Dataminr’s 12+ year event and public data archive, and scans the broader public internet at large.

Unlike task-based AI systems, Intel Agents are goal-oriented—empowered to make autonomous decisions about where to look and the relevance of what they find. A multi-agent Agentic AI workflow empowers AI agents to act independently, orchestrate collaboratively, and synthesize a comprehensive contextual intelligence picture. This fleet of autonomous AI agents operates at a scale and speed that no human team—regardless of size—could ever accomplish. It scans across and synthesizes from zettabytes of data in mere seconds to deliver critical context for every event, risk, and threat when it’s first detected and as it unfolds. Augmenting Dataminr’s ReGenAI Live Briefs, Intel Agents write out this real-time context within the live Dataminr product.

Source: Dataminr

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Unlike basic chat assistants, Legion enables permissioned software agents that call approved tools and data, coordinate with one another, and take auditable actions—shortening the Observe–Orient–Decide–Act (OODA) cycle and reclaiming administrative time.

“Chat has been a great on‑ramp for AI, but agents will be the real force multiplier for the Air Force and the Department of War,” said Ben Van Roo, co-founder and CEO of Legion Intelligence. “Like drones in Ukraine have changed the physical landscape of war, thousands of agents will soon augment workflows across a variety of use cases—from kill chains to staff officers. Our focus is enabling that future, with the software and tooling to build and manage the agentic future.”

Through allow‑listed connectors and data gateways, Legion is designed to embed alongside legacy maintenance, personnel, planning, and operations applications, engage data applications and alerts (e.g., Dataminr), work across productivity (SharePoint, Teams, Jira), and interoperate with systems of record (e.g., Oracle, Palantir)—with row/column controls, provenance, and end‑to‑end audit.

Source: Legion Intelligence

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By Austen Bruhn, DoD/NatSec Architect, Coder

The U.S. Intelligence Community stands at a turning point. The global race for AI supremacy is no longer theoretical. Agentic systems are already in use, adversaries are moving fast, and the mission window for secure adoption is closing.

Around the world, near-peer competitors are using agentic AI to speed up software deployment, automate cyber operations, and reshape how intelligence is built and used. These tools generate and execute code, act independently, and handle development tasks that used to require full teams. The U.S. cannot fall behind—not on speed, and not on security.

At home, federal guidance is pushing forward. Executive Order 14179 and memos like M-24-10 call for bold AI adoption with real guardrails. The IC may not be directly bound by them, but the direction is clear. The mandate is to deploy AI systems that are governed, auditable, and trusted from the start.

The call to action is clear and coming from inside the community: “Entities that augment their activities with AI applications will likely disrupt those that do not,” said CIA Chief Cyber Policy Adviser Dan Richard. CIA Chief AI Officer Lakshmi Raman added, “AI is not just an emerging technology. It is a strategic necessity.” This imperative is not abstract; it lands directly on the desks of IC code builders, who must adapt their workflows to leverage agentic AI securely.

Together, these perspectives underline a common truth: the IC cannot afford to delay. Adoption is inevitable, but it must proceed with an implementation approach that ensures trust, accountability, and security from the start.

Adversaries are already applying AI to accelerate code generation for cyber operations and software deployment. Reporting shows that China’s military innovation agenda emphasizes “intelligentized” systems, including AI-driven software development and autonomous cyber tools (Brookings). For the IC, this raises the stakes: the competition is not just about who fields AI first, but who does so securely. If builders in the IC remain constrained by legacy, decentralized environments while adversaries leverage agentic AI for rapid development, the U.S. risks falling behind in both speed and resilience. This is why secure, centralized, and policy-aligned agentic development environments are a strategic necessity now, not later.

Agentic AI adoption is inevitable. The mission now is to implement it safely, at speed, and at scale.

The opportunity and risk of agentic AI software development

Agentic AI offers builders powerful new capabilities: reading documentation, generating shell scripts, proposing code, and even testing and deploying microservices. For the IC, this means accelerated software development, increased automation within Continuous Integration/Continuous Deployment (CI/CD) pipelines, and freeing mission teams to focus on higher-order analysis.

But with great capability comes new vulnerability. These systems challenge traditional software lifecycle boundaries and introduce risky behaviors, such as:

• Accessing sensitive code repositories unintentionally
• Using tools beyond their approved scope
• Exposing sensitive data through verbose or unreviewed outputs
• Escalating privileges, altering configurations, or attempting unauthorized external communications

These examples illustrate the emergent behaviors that make agentic AI risky in mission environments. They directly inform the safeguards outlined later in this article, from immutable audit logs and toolchain limits to human-on-the-loop oversight and continuous evaluation.

In practice, this means builders must anticipate edge cases rather than wait for them to appear in production. For example, an agent trained to optimize workflows might unintentionally bypass a security step to increase efficiency. Without structured oversight, such a behavior could introduce vulnerabilities into classified systems. Builders are therefore on the frontlines of ensuring guardrails are not just theoretical but actively enforced through controlled environments, continuous monitoring, and deliberate design choices.

From human-in-the-loop to human-on-the-loop

As the volume and velocity of sensor and intelligence data continues to surge, the traditional human-in-the-loop model is reaching its limits. For time-critical operations, humans can no longer process and act on information fast enough. This necessitates a shift toward human-on-the-loop systems, where automated processes execute within defined parameters, and humans focus on strategic oversight, operational boundaries, and ethical constraints.

These practices must be supported by software systems that are adaptive and resilient. Builders should adopt AI-forward methodologies and automation frameworks that maintain rigorous security and governance, including explainable outputs, audit trails, and fail-safe policies. The goal isn’t full autonomy—it’s controlled autonomy.

Raman echoed this balance of oversight and partnership, saying the CIA’s broad approach to AI is focused on “how humans and the AI are working together,” with humans ultimately responsible for oversight, accountability, and intervention when necessary.

Moving from philosophy to practice, this shift in human oversight does not occur in a vacuum. In IC workflows, human-on-the-loop oversight means builders and operators may not review every line of AI-generated code, but they validate that outputs adhere to policy, confirm auditability, and ensure systems cannot access unauthorized data. This balance enables time-critical missions to run at machine speed while keeping accountability and intervention authority firmly in human hands. It is unfolding alongside federal policy designed to accelerate AI adoption responsibly. For the IC, the challenge is aligning this operational reality with governance expectations now shaping the broader federal landscape.

Policy to practice

Federal policy is pushing agencies to adopt AI responsibly and at speed. M-24-10 and M-25-21 highlight the government’s intent to balance innovation with governance. The IC is not bound by these memos, but they set expectations and signal how oversight bodies, Congress, and the public will judge whether the IC is deploying AI effectively and responsibly. The challenge is translating these high-level policies into secure implementation approaches inside classified environments.

The IC’s own Principles of AI Ethics reinforce these expectations: development must be human-centered, accountable, secure, and science-informed. These priorities also align with broader federal standards work led by the National Institute of Standards and Technology (NIST). The AI Risk Management Framework (AI RMF 1.0) emphasizes trustworthy AI through governance, transparency, and continuous monitoring—principles reflected in the safeguards outlined in the next section.

Likewise, Special Publications (SP) 800-218 and 218A stress secure software development practices, code integrity, and supply chain protection, all of which map directly to the IC’s need for rigorous DevSecOps pipelines, audit logging, and boundary enforcement in AI-augmented environments. What follows is focused on implementation approaches: how the IC can translate these principles into operational safeguards in builder workflows.

Securing the development environment

Operationalizing AI agentics for builders within classified or high-sensitivity environments demands a new set of controls:

Control boundaries

Boundary of place – isolate environments: Sandbox agents in hardened, network-limited environments.

Boundary of tools – enforce toolchain limits: Define explicit tool access policies, and block everything else.

Boundary of data – enforce agentic boundaries: Restrict data and system access to prevent unauthorized queries or lateral movement.

Monitoring and detection

Audit all actions: Track all executions to maintain accountability and transparency.

Detect and respond to threats: Forward immutable logs into Security Information and Event Management (SIEM) systems for automated detection of misuse or compromise.

Integrate into DevSecOps (development, security, and operations): Ensure pipelines are AI-aware, scanning generated code and architectures for malicious or insider threat behavior.

Use AI gateway proxies: Enforce data loss prevention (DLP) and inference monitoring for drift, poisoning, prompt injection, hallucination, malicious code, and data leakage.

Operational oversight

Human-on-the-loop verification and evaluation: Maintain human oversight through post-hoc audits of AI-generated code and runtime behaviors.

Raman emphasized that boundaries are critical for ensuring compliance with legal policy and data protections. This underscores the need for agentic boundaries that prevent unauthorized data access and enforce compartmentalization across environments.

Steve Schmidt, Chief Security Officer at Amazon, underscored the accountability challenge of deploying agentic systems: “How do we make sure that the software is doing exactly the right thing every single time, and more importantly, that we can prove what it did to stakeholders and regulators?”

Run dangerous things in a safe place

Coder provides one example of how secure-by-design tools can support the IC’s adoption of AI agentics. Features such as Agent Boundaries (policy-enforced sandboxes that define what an agent can access) and Tasks (auditable, human-verifiable subtasks) demonstrate how AI-augmented coding can be implemented while preserving oversight and compartmentalization.

Another critical safeguard is centralizing access to both AI models and the compute resources that power them. One underappreciated risk is that agentic AI itself can act as a new form of insider threat. A malicious prompt injection or poisoned retrieval source can cause an otherwise trusted agent to generate backdoors, disable safeguards, or leak sensitive data. For the IC, the stakes are even higher: code deployed in classified environments must assume the agent could be compromised. Centralized environments, logging all actions, and enforcing compartmentalized access are essential defenses against both human insiders and AI behaving like insiders. Decentralized, ad hoc environments multiply the risks of misconfiguration, exfiltration, and uneven enforcement. Centralized environments, such as those enabled by Coder, give agencies the ability to enforce boundaries, monitor agentic behavior, and apply consistent controls for both builders and AI agents within a single secured infrastructure.

Yet even the most secure implementations cannot exist in silos. Builders may operate within agency-specific environments, but the risks and safeguards around agentic AI cut across the entire IC. Scaling these safeguards requires more than technical controls. It requires alignment, coherence, and oversight.

Scaling agentic AI across agencies

Each IC agency has distinct missions, data needs, and operational realities, so managing and deploying agentic platforms must remain within their domain. At the same time, the Office of the Director of National Intelligence (ODNI) will set strategic guidance and standards, emphasizing guardrails and oversight rather than control. Looking ahead, Director of National Intelligence Gabbard noted that ODNI 2.0 will “enable ODNI to focus on fulfilling its critical role of serving as the central hub for intelligence integration, strategic guidance, and oversight over the Intelligence Community.”

Coherence across the IC does not mean uniform platforms; it means shared baselines and reciprocal trust. ODNI should establish minimum expectations for red-teaming, continuous monitoring, and auditability. Agencies may deploy different infrastructure, but all systems should still produce logs compatible with a common oversight framework. Shared playbooks for threat detection, standardized reporting of AI incidents, and reciprocal validation of controls would allow the IC to scale innovation while avoiding fragmentation, even as it scales back in size. While ODNI cannot enforce reciprocity for approvals across agencies, it can still establish common frameworks and best practices to ensure a successful adoption of agentic AI across the IC.

Securing the future of AI in the IC

Agentic AI represents a new frontier for builders in the Intelligence Community. But with great autonomy comes greater risk.

The path forward is clear: agencies must adopt secure-by-design environments, integrate AI-aware DevSecOps practices, and enforce controls like boundaries, proxies, and immutable audit logs. Tools such as Coder Boundaries and Coder Tasks provide practical mechanisms to operationalize these safeguards while preserving human accountability and oversight.

ODNI’s role is to provide the ethical and strategic guardrails, but execution must remain federated, managed by each agency in line with its unique mission. The goal is coherence across the IC, not centralization.

Agentic AI is essential. The IC must adopt it boldly but with discipline, within trusted security approaches. The cost of inaction is high: adversaries will not wait. It’s time to move from experimentation to secure execution.

About the Author

Austen Bruhn is the DoD/IC technology strategist and architect at Coder, bringing deep expertise in secure AI adoption and DevSecOps transformation. With experience developing Lockheed Martin’s edge AI systems, deploying Red Hat’s OpenShift classified environments, and now enabling agentic AI workflows for DoD/IC missions, he’s helped agencies navigate the critical imperatives of innovation and security. From hands-on experience from on-orbit Kubernetes deployments to IC-wide AI architectures, Austen focuses on translating federal AI and software development policy into operational reality while maintaining the security posture that national security demands.

About Coder

Coder is the AI software development company leading the future of autonomous coding. Coder helps teams build fast, stay secure, and scale with control by combining AI coding agents and human developers in one trusted workspace. Coder’s award-winning self-hosted Cloud Development Environment (CDE) gives teams the power to govern, audit, and accelerate software development without trade-offs. Learn more at coder.com.

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The post Builders at the Frontline: Safeguarding Agentic AI in the Intelligence Community appeared first on Intelligence Community News.

This AI innovation introduces a new generation of AI-powered real-time information that enables private and public sector organizations to supercharge their abilities to more effectively navigate a world of constant change—revolutionizing how Dataminr users rapidly understand, contextualize, and respond to unexpected events, according to the company.

“Intel Agents transform the category of real-time information, providing our clients with the surrounding context needed to respond faster and more effectively to unfolding events, risks, and threats,” said Ted Bailey, founder and CEO of Dataminr.

Intel Agents build on top of Dataminr’s 2024 release of ReGenAI, a breakthrough form of Generative AI that automatically regenerates live event briefs in real-time as events unfold. Intel Agents augment ReGenAI event briefs with a new layer of real-time context that is uniquely made possible by Agentic AI. As events unfold, Intel Agents work continuously and collaboratively, analyzing new emerging developments as they happen, and updating the surrounding context that clients need.

“When Dataminr’s AI Platform detects the earliest indications of events, risks, and threats, Intel Agents are tasked with autonomously determining the additional context that’s needed, where to look for it, and how to best synthesize what they find into concise text. The unique decision-making capabilities of Agentic AI, powered by Dataminr’s proprietary LLMs and AI Platform, enable new innovations that were never before possible,” added Alex Jaimes, Dataminr’s chief AI officer.

Intel Agents are powered solely by Dataminr’s internally developed and operated LLMs—all trained on Dataminr’s proprietary 15-year data and event archive. Intel Agents seamlessly fuse relevant information from external public sources with rich insights from internal data sources, including the related historical and current real-time events only available within Dataminr’s AI Platform. Dataminr’s unmatched event archive is relied on not only to train the LLMs powering Intel Agents, but also to enrich the context of real-time events, threats, and risks in powerful ways, the company said.

Source: Dataminr

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Soleite CoPilot is currently deployed in support of the U.S. Navy, where it enhances sustainment, acquisition, and logistics workflows by automating routine tasks, improving data access, and enabling faster, more informed decision-making. Built for secure and disconnected environments, Soleite CoPilot is fully compliant with IL5/IL6 requirements and supports operations in air-gapped and DDIL (denied, degraded, intermittent, and limited) conditions.

“Soleite CoPilot isn’t just another chatbot, it’s a mission-ready intelligence layer that fits directly into how the federal workforce operates,” said Lewis Harris, vice president of growth at Greystones Group. “It reflects years of experience serving the Navy, built with federal users, security standards, and operational realities in mind.”

Soleite CoPilot integrates directly into Microsoft Teams, SharePoint, and other commonly used DoD platforms. It uses retrieval-augmented generation (RAG) and multi-agent orchestration to deliver real-time insights, compliance validation, document automation, and task coordination through natural language interfaces.

The platform was designed to ensure government ownership of data, models, and workflows, and is model-agnostic, able to operate with both open-source and proprietary LLMs depending on the security and mission environment.

Soleite CoPilot was developed under a DoD SBIR Phase III award, which enables it to be acquired non-competitively under existing SBIR authority. Its “awardable” designation on the Tradewinds Solutions Marketplace means that agencies can acquire the solution quickly, securely, and without recompeting, using any type of funding.

Source: Greystones Group

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