Production-grade AI agents
for enterprise workflows
Dezifi helps enterprises move from AI demos to governed, observable, and reliable AI execution across Salesforce, Jira, Slack, GitHub, ServiceNow, and internal systems.
Connects with the systems your teams already use
AI demos are easy. Production execution is hard.
Most AI initiatives work in controlled demos. In real workflows teams hit governance, reliability, debugging, and accountability walls.
Unpredictable behavior
Agents act differently across runs with no clear constraints.
No visibility
Business and engineering teams cannot see what happened or why.
Unrestricted access
Security teams worry about agents touching sensitive systems.
No audit trail
Approvals, decisions, and tool calls are not logged for review.
One platform for orchestration, governance, and observability.
Dezifi provides the infrastructure to design, deploy, monitor, and govern AI agents across enterprise workflows.
Agent Orchestration
Design structured workflows with tools, conditions, approvals, and multi-step execution.
Governance & Policies
Define what agents can access, what actions they can take, and when humans must approve.
Observability
Track every agent run with complete traces, logs, latency, cost, and tool usage.
Enterprise Integrations
Connect agents to CRMs, ERPs, engineering tools, communication systems, databases and APIs.
Human-in-the-Loop
Approvals, reviews, and escalations before high-risk actions execute.
Multi-Agent Architecture
Specialized agents with their own roles, tools, policies, and execution boundaries.
Design agents with structure, not vibes.
A 9-step builder defines the agent's identity, models, tools, knowledge, memory, voice, guardrails, and governance — every agent ships production-ready.
- System prompt, reasoning pattern, output format
- Multi-model: GPT, Claude, Gemini, fine-tuned LLMs
- Tool & integration binding with scoped permissions
- Memory, RAG knowledge bases, voice and guardrails
Multi-agent workflows, visually orchestrated.
Compose triggers, agents, decisions, and human approvals on a canvas. Run them as Workflow, Map-Reduce, or Manager-Worker patterns.
- Triggers: Webhook, Email, Schedule, Slack, Manual, Chat
- Branching, parallel fan-out, and approval gates
- Versioned graphs with rollback and change history
- Live test runs with full execution lineage
Policy-as-code for every agent action.
Define what agents can do, what they can touch, and how strictly limits are enforced — at the workspace, agent, or workflow scope.
- Tool allow/deny lists and scoped credentials
- PII isolation and data privacy rules
- Token, time, and cost execution limits
- Hard or soft enforcement modes with audit trail
Runtime safety checks that actually block.
Pick a protection tier or build your own. Guardrails inspect every prompt, tool call, and response — blocking dangerous content, leaks, and policy violations in real time.
- Permissive · Balanced · Strict — pre-built tiers
- PII detection, jailbreak detection, output validation
- AI-powered detection for regulated industries
- Per-agent overrides and live test playground
Every run, fully traceable. Every tool call, logged.
Production-grade observability designed for AI agents — see runs by status, drill into spans, replay decisions, and debug failures the way SREs debug services.
- Status filters: total, running, succeeded, failed, pending
- Per-run latency, cost, token, and tool span timeline
- Approval & escalation history with reviewer identity
- Searchable by Run ID, agent, workflow, or workspace
Operational metrics for the AI workforce.
Aggregate volume, success rate, latency, token use and cost across agents and workflows. Slice by 24h, 7d, or 30d. Plug into your existing BI.
- Daily run outcomes, cost & token consumption
- Per-agent and per-tool performance breakdown
- LLM call distribution across providers and models
- Export to Snowflake, BigQuery, or your warehouse
Start with one workflow. Scale across the enterprise.
Teams use Dezifi to operationalize AI agents in the workflows that matter most — then expand.