Describe an agent in plain English — the engine builds it, governed from the first call. Local LLMs, autonomous and self-learning agents, the agentic mesh: GovEngine is the governed core agencies run the whole frontier through. Any model, any cloud.
Every claim on this page resolves to a running system. This is the live policy-enforcement stream on /v1.
Tell GovEngine what you need in plain English. It assembles the agent — picks the model, wires the federal connectors, attaches the policy gates, sets up memory and human approval — and hands you something governed from its very first call. No code, no separate accreditation.
Each capability orbiting the core above is the same story: a frontier idea most agencies can't safely adopt yet — and exactly how GovEngine makes it deployable. Inner ring is live today; outer ring is the emerging frontier.
Small and open models running on-prem, in GovCloud, or fully air-gapped — sovereign inference with zero data egress.
Agents that plan, call tools, and act toward a goal with minimal oversight — autonomy levels 3–4.
Agents that set their own sub-goals and adapt from outcomes instead of following a fixed script.
Anthropic, GPT, Gemini, Llama, on-prem — swap the underlying model without re-accrediting the agent.
Multi-agent coordination — the emerging "internet of agents" connecting specialized agents across teams.
Lifelong agent identity and memory — knowledge graphs (GraphRAG) that persist across sessions.
Agents whose entire job is to oversee other agents at runtime — Gartner's fastest-growing agentic category.
The newest Model Context Protocol: elicitation forms, sampling with tool use, and agent task management.
The advantage of a governed core is that new agent capabilities arrive already controlled. Here's what's landing — and where GovEngine meets it.
The Model Context Protocol added elicitation (agents request structured human input mid-run), sampling with tool use, and task management (list / cancel long-running agent work). GovEngine targets these at the runtime so the human-approval gate is native, not bolted on.
The first peer-reviewed threat model for autonomous, tool-using agents — covering tool misuse, memory poisoning, and identity abuse. The engine's gates map directly onto its categories, giving agencies a named framework to certify against.
Enterprises are moving from single agents to coordinated multi-agent systems — the "internet of agents." Governance becomes the hard part: a mesh is only as safe as the layer every agent-to-agent call passes through.
82% of government organizations already report using AI agents, with leaders saying they're outpacing the private sector. The bottleneck has shifted from deploying agents to governing them at scale.
Gartner projects 80% of governments running decision agents by 2028 and guardian-style oversight reaching 10–15% of the agentic market by 2030 — because at multi-agent speed, humans can't keep up without an engine watching.
Credibility with a federal buyer comes from never overclaiming. Here is the exact line.
Every agency must maintain an AI use-case inventory and risk-manage high-impact AI. Agencies reported 3,000+ use cases in 2025 — assembled by hand, in spreadsheets. Nobody auto-exports it from real enforcement data. Describe it once to the Agent Engine, and because we already capture every call, model, cost, guardrail fire, and redaction, the inventory keeps itself current — the line that makes a CAIO lean forward.
Don't sell another model or another agent. Be the engine every agent is built in — and governed by.