Observability & Governance

Every step visible.
Every step governed.

Leena AI's Responsible AI layer is built into every AI Colleague - not bolted on, not an enterprise tier,
not an add-on. Full audit trail, runtime guardrails, and RBAC synced from your systems of record.

What it is

Responsible AI, built in.

Every AI Colleague ships with both layers - no separate edition, no upcharge,
no compliance scramble three months in.

Observability

What did the AI Colleague do?

See every reasoning step, tool call, and fallback — logged in real time and replayable on demand.

Governance

What is it allowed to do?

Decide the limits up front. Off-policy actions are stopped before they ever reach a real system.

Other systems flag it. Ours never lets it happen.

How it works

Two layers. One architecture.

Both ship by default in every AI Colleague.

Observability

See everything your AI does.

In real time. Replayable on demand.

Knowledge Health

Stale articles, broken links, conflicts, and misaligned permissions - caught before agents act on them.

Debugging Console

Forensic, turn-by-turn replay of any request: tool discovery, reranking, LLM reasoning, injected instructions, final output. Look up by Request or User ID across web, Slack, SMS, voice, eval, and system.

Analytics & Helpdesk Insights

Success/failure rates, handle time, automation vs. handoff - by AOP, team, and region. Plus ticket clustering, cluster analysis, auto-drafted KB articles, and open-ticket impact.

Cost & Performance Telemetry

Token cost and step-level latency, exposed. See where the money goes and where the time goes.

Eval Suite

Validate any AOP before it goes live. Auto-generates test cases - happy path, edge, negative, adversarial - from the AOP's own config, then replays and scores them. Catch the regression before your users do.

Governance

Control what your AI can do.

Bad actions stopped before they happen.

Multi-layer Guardrails

Org-wide checks enforced at four layers - input, plan, tool call, and output. PII handling (mask / block / log), moderation, and jailbreak and prompt-injection defense, enforced before execution continues. Every violation logged.

Deterministic Tools

AI Colleagues call validated APIs with validated fields - never improvised ones. No hallucinated APIs, no surprise side-effects.

Second Evaluator LLM

One model drafts. A second evaluator LLM checks every output before it ships. Hallucinations caught before users see them.

RBAC That Mirrors Your Org

Synced from Active Directory and your HRIS. When a user's permissions change in the source system, the Colleague's change with them.

Inherited Permissions

Source systems stay authoritative. A Colleague never sees or does more than the user it acts for.

Why it matters

AI that takes action is a different
category of risk.

AI that acts carries different risks than AI that advises. One bad step cascades at machine speed.
Regulators aren't asking if you have oversight, but how deep it goes.

Action-safe under autonomy

Colleagues stay inside the rails you set. Off-policy actions are blocked, not flagged after the fact.

Continuous compliance

Audit trails on every run. Compliance is continuous instead of periodic - no quarterly scramble, no manual reconciliation.

Speed without permission slips

Trust is already there, not something to prove every quarter. Your team moves faster, with the oversight already done.

“AI governance will be mandatory under every
sovereign AI regulation by 2027.”

— Gartner®

What's different

Governance shouldn't be the
slide after the demo.

Most vendors treat governance as an enterprise tier, a roadmap item, or a checkbox after the
deal closes. Leena AI's governance is in the architecture from the first Colleague you turn on.

Built in, not bolted on

Every Colleague ships governed by default. No "governance edition," no add-on, no integration project. Same controls whether you have 50 users or 50,000.

Four layers of enforcement, not one

Most vendors check policy at the prompt. We enforce at input, plan, tool call, and output. A single check isn't enforcement - it's wishful thinking.

Deterministic execution, not LLM improv

Skills hit known systems with validated fields. No hallucinated APIs, no surprise side-effects. The LLM reasons; it doesn't improvise the system call.

The why, not just the what

Reconstruct any run end to end - prompts, tool calls, fallbacks, evaluator checks. The full reasoning trail, not a final-state log.

Breakdown

Inside the Agentic AI architecture

Touchpoints

Your people work in Teams, Slack, email, voice, browsers, portals. AI Colleagues show up there. Not somewhere else.

8+ channels. Zero context switching.

Same agent, same memory, every surface

Talks back. Reaches out. Doesn't wait to be asked.

Orchestrator

The brain. Reads the request, builds the plan, calls the right AI Colleague, routes between models on the fly. Model-agnostic by design - runs on Claude Opus 4.8, WorkLM™️, GPT 5.5, Llama 4, or Gemini 3.5.

Plans are built, not pre-coded

Breaks complex asks into doable subtasks

Hands off cleanly between agents over A2A

AI Colleagues

Level 3 digital workers, each grounded by Agent Operating Protocols (AOPs), equipped with Tools to act in enterprise apps, powered by Context Graph and Memory, and managed via a Workbench.

Always on. 24/7. No human trigger.

Gets smarter with every interaction via the Context Graph

Handle exceptions like a person would. No "I didn't understand."

Studios

Three no-code studios that let business users design, assemble, and ground AI Colleagues in plain English. AOP Studio writes the process. Workflow Studio wires in the tools. Knowledge Studio connects the truth.

Kickoff to live in days, not months

Business users own and iterate without engineering

1000+ pre-built tools across 200+ enterprise systems

Permissions and Access Controls

AI Colleagues only see and do what their role allows - across every system you connect. Permissions stay tied to your existing tools.

Inherits from your IdP. No re-mapping.

Every action audit-logged and identity-bound

Safe for multi-team, multi-tenant deployments

Integrations

200+ pre-built enterprise connectors across ServiceNow, Workday, SAP, Oracle, Salesforce, UKG, SharePoint, Snowflake, and more — via APIs, MCP, A2A, and browser/RPA.

200+ live on day one

Connect in minutes, no custom code

API + RPA + browser fallback. Nothing's "we can't integrate."

Observability and Governance

Responsible AI layer, built into every AI Colleague. See every step, govern every action. Dashboards for execs, ops, and risk.

Full trace: what the agent did, why, what it read

Guardrails at every layer - enforced before execution

Eval Suite catches regressions. Quality trends up, not sideways

Trust and Security

Agentic AI security built into the architecture, not bolted on. SOC 2, ISO 27001, HIPAA, AES-256-GCM.

AES-256 at rest, TLS 1.2+ in transit, AWS KMS-managed keys

Shared, single-tenant, or private VPC across 14+ regions

SSO, MFA, and RBAC for staff and customer admins

Pick your next stop

Hand-picked next reads — short on filler, long on what matters.

Agentic AI Architecture

The platform Fortune 500s use to build, govern, and run enterprise AI Colleagues.

8 Years of Integrations

One integration layer, 200+ systems of record, battle-tested in 500+ enterprises over 8 years.

Avoid Vendor Lock-In

No OEM money. No vested interest. No vendor lock-in. We connect to all of them.

Leena AI Documentation

Your reference for configuring, deploying, and managing AI Colleagues at scale.

Frequently asked questions

What is AI observability?

The ability to see exactly what an AI Colleague did, why it did it, and what it acted on - every reasoning step, tool call, and fallback. In Leena AI, each run is logged in real time and replayable on demand through the Debugging Console.

What is AI governance?

The rules that decide what an AI Colleague is allowed to do - what it can access, which tools it can call, what actions it can take. Off-policy behavior is blocked at runtime, before it touches a real system, not flagged after the fact.

How is observability different from governance?

Observability shows you what an AI Colleague did. Governance decides what it's allowed to do. You need both - observability without governance is just a recording of preventable mistakes. Every Colleague ships with both layers by default.

How does Leena AI prevent AI hallucinations?

Two LLMs run on every output. One drafts the response or action; a second evaluator LLM checks it before it ships. Combined with deterministic skills that call validated APIs with validated fields, hallucinated facts and invented tool calls are caught before they reach a user or a system.

How do AI guardrails work in Leena AI?

Org-wide checks are enforced at four layers - input, plan, tool call, and output - not a single check at the prompt. They cover PII handling (mask, block, or log), moderation, and jailbreak and prompt-injection defense, enforced before execution continues. Every violation is logged, and a failure at any layer halts the action.

What is RBAC for AI Colleagues?

Role-based access control applied to AI. Every Colleague inherits the permissions of the user it's acting for, synced live from Active Directory and your HRIS. Source systems stay authoritative - a Colleague never sees or does more than that user. When their permissions change, the Colleague's change with them.

Can my team replay a specific run for an audit?

Yes. Every run is replayable end to end through the Debugging Console — turn by turn, from tool discovery and reranking through LLM reasoning, injected instructions, and final output. Look up any run by Request or User ID across web, Slack, SMS, voice, eval, and system.

What is the Eval Suite?

A pre-deploy validation layer. It auto-generates test cases - happy path, edge, negative, and adversarial - from an AOP's own config, replays them under evaluation, and scores six LLM-judged metrics: hallucination, all items processed, steps in order, right tools and parameters, clean user communication, and expected outcome. Catch the regression before your users do.

Is governance a separate add-on or enterprise tier?

No. Every AI Colleague ships governed by default - same controls, same enforcement, same audit trail across every plan, whether you have 50 users or 50,000. It's built into the architecture, not a SKU, an add-on, or an integration project.

Why does AI governance matter for enterprises?

AI that acts carries a different category of risk than AI that advises - a wrong action can leak data, change a record, or hit compliance, and one bad step cascades at machine speed. Gartner expects AI governance to be mandatory under every sovereign AI regulation by 2027. Governance is how you contain that risk.

Every step visible. Every step governed.