Why AgentLattice
AI agents are writing code, processing payroll, approving invoices, and deploying to production. They operate with real credentials, access real data, and make real decisions. But unlike every human employee in your organization, they have no governance layer.
When an AI agent merges a pull request, runs a database migration, or accesses customer records, you cannot answer basic questions that any auditor will ask: Who authorized this action? What data did the agent access? Was there segregation of duties? Where is the audit trail?
These are not hypothetical concerns. If your organization is subject to SOX, HIPAA, FINRA, PCI-DSS, or GDPR, unanswered questions about autonomous AI actions are compliance violations waiting to happen.
AgentLattice is the governance and compliance layer for AI agents. It sits between your agents and the actions they take, providing the same controls you already apply to human operators: identity, authorization, audit trails, and segregation of duties.
What AgentLattice Does
AgentLattice governs AI agent actions across any domain — software engineering, finance, healthcare, HR, legal, and beyond. It does four things:
Identity. Every agent action is cryptographically attributed to a specific operator configuration, registered to a specific workspace. Not "an AI did this" — this agent, authorized by this admin, acting under this policy.
Authorization. Policies define what each agent can and cannot do. You control which action types are allowed, which require human approval, and which are denied outright. Policies support conditions, so you can auto-approve low-risk actions while gating high-risk ones.
Audit trail. Every action — approved, denied, or timed out — is recorded in a tamper-evident log. The trail captures what action was attempted, what data was accessed (metadata, never raw PII), which policy governed the decision, and who approved or denied it. This log is exportable for compliance teams and auditors.
Segregation of duties. The entity that proposes an action cannot be the entity that approves it. When a policy requires approval, the agent's action is held in a queue until an independent human reviewer approves or denies it. This is the core SOX control, applied to AI agents.
Who AgentLattice Is For
VP of Engineering evaluating how to let AI agents contribute to production systems without creating unauditable black boxes. You need to know that every agent-authored code change, deployment, and database migration has a clear chain of accountability before it executes.
CISO and compliance officers who need to demonstrate to auditors that AI agent actions are governed by the same controls as human actions. You need exportable audit trails, policy enforcement evidence, and segregation of duties documentation.
Platform and DevOps teams responsible for integrating AI agents into existing workflows. You need an SDK that drops into your agent code in minutes, not a six-month infrastructure project. AgentLattice provides TypeScript and Python SDKs, plus framework integrations for LangChain and other orchestration tools.
Operators running AI agents in regulated industries — finance, healthcare, legal — where every automated decision must be traceable, authorized, and reviewable. AgentLattice provides the governance substrate that makes AI agent deployment defensible to regulators.
What Changes When You Adopt AgentLattice
Before AgentLattice, your AI agents operate in a trust vacuum. They have credentials, they take actions, and the only record is whatever logging you built yourself — if you built any at all.
After AgentLattice:
Every agent action is audited. Whether the agent commits code, reads a credential, triggers a deployment, or runs a migration, the action is recorded with full context: who, what, when, why, and under what authority.
Policies enforce rules automatically. You define what agents can do, and the platform enforces it. An agent that tries to access data outside its authorized scope gets denied before the action executes, not after the damage is done.
Approvals gate risky actions. High-risk actions — production deployments, database migrations, credential access — require human approval before they execute. The agent proposes, a human reviews, and only then does the action proceed. The approval decision is part of the audit trail.
Agents self-correct. When a policy denies an action, AgentLattice tells the agent exactly which rule fired and why. Smart agents use this feedback to adapt — splitting a too-large PR into smaller ones, requesting a narrower data scope, or escalating to a human. Governance becomes a feedback loop, not just a wall.
Delegation is scoped and temporary. When an agent spawns sub-agents, each child inherits only the capabilities you explicitly grant, with automatic expiration. No privilege escalation, no orphaned credentials, no scope creep.
The Four Governance Primitives
AgentLattice is built on four primitives that apply identically whether the agent is merging a pull request or processing an insurance claim:
Identity ensures every action is attributed to a specific, registered agent configuration — not just "an AI" but a named operator with cryptographic proof of origin. Authorization enforces policies that control what each agent can do, with conditions that distinguish between a 10-line code commit and a 500-line database migration. Audit trail captures every action in a tamper-evident log that satisfies SOX auditors, HIPAA compliance officers, and security teams. Segregation of duties enforces the principle that the entity proposing an action is never the entity approving it — the foundational control that makes AI agent governance defensible in any regulatory context.
These four primitives are not features bolted onto an existing product. They are the product. Everything AgentLattice does — the SDK, the dashboard, the policy engine, the approval workflows — exists to make these four primitives work reliably at scale.