The runtime for accountable AI execution
Dezifi unifies agent design, workflow orchestration, integrations, policy enforcement, and observability into a single control plane built for enterprise scale.
Agent Builder
Build specialized agents for different teams and workflows.
- Role-based agent setup
- Prompt & instruction management
- Tool assignment
- Policy binding
- Approval configuration
- Workflow mapping
- Context injection
- Memory & knowledge configuration
Workflow Orchestration
Design reliable AI workflows beyond simple chat.
- DAG-based execution
- Multi-step workflows
- Conditional branching
- Parallel & fan-in/fan-out
- Retries & fallbacks
- Escalation paths
- Scheduled & event-triggered runs
Tool & System Integration
Connect agents to enterprise applications.
- Salesforce, Jira, GitHub, Slack
- Gmail, Outlook, Teams
- ServiceNow, HubSpot, Zendesk
- SQL, Snowflake, Sheets
- REST APIs & webhooks
- Internal tools & custom systems
Governance & Policy Engine
Control what agents can do.
- Tool-level access control
- User-level permissions
- Business rule policies
- Approval gates
- Risk-based execution controls
- Sensitive action restrictions
- Environment boundaries
- Audit-ready logs
Observability
Debug and monitor agents like production systems.
- Full run traces
- Tool & model logs
- Latency & cost tracking
- Token usage
- Error visibility
- Failed step diagnosis
- Approval history
- Output review
Human-in-the-Loop
Add human approvals where needed.
- Approval before external action
- Review before CRM update
- Escalation to team members
- Exception management
- Manual override
- Review queue
One lifecycle for every agent
AI demos are easy — production execution is hard. Dezifi treats agents like production software: built with structure, governed by policy, orchestrated deliberately, and operated with full visibility.
Build
Configure an agent in the 9-step builder — model, tools, skills, knowledge bases, memory, and guardrails. Test it in chat with a full execution trace before anything ships.
Govern
Bind policies that scope every tool call, add approval gates for high-risk actions, and attach runtime guardrails that block unsafe behavior instead of just logging it.
Orchestrate
Compose agents into multi-step workflows on a visual canvas — branching, retries, parallel fan-out, scheduled triggers, and human checkpoints where the stakes demand them.
Operate
Monitor every run, trace every tool call, evaluate quality against datasets, and track cost, latency, and drift with workspace-level analytics.
Frequently asked questions
What teams evaluating an enterprise AI agent platform ask us most.
- What is an AI agent platform?
- An AI agent platform is the runtime, orchestration, and control layer for autonomous AI agents in production. Dezifi covers the full lifecycle: building agents, connecting them to enterprise tools, enforcing policies and guardrails at runtime, orchestrating multi-agent workflows, and observing every run.
- How is Dezifi different from an agent framework like LangChain or CrewAI?
- Frameworks give developers libraries to code agents; Dezifi is a managed platform with governance, observability, evaluation, and multi-tenant isolation built in. Teams configure agents in a visual builder, bind policies as code, and get audit trails without building that infrastructure themselves.
- Do I need to write code to build an agent?
- No. Agents are configured in a 9-step visual builder — model, tools, skills, knowledge bases, memory, voice, and guardrails. Developers can still go deeper with custom tools, REST APIs, webhooks, and the SDK when a workflow needs it.
- Which LLM providers does Dezifi support?
- OpenAI, Anthropic, Google, AWS Bedrock, Azure OpenAI, and locally hosted models. Each agent runs on a single primary model, and workflows can mix providers across agents.
- Can Dezifi run multi-agent workflows?
- Yes. Workflows orchestrate one or more agents as a DAG with conditional branching, parallel execution, retries, fallbacks, scheduled and event-based triggers, and human approval steps between agents.
- How do teams usually start?
- With one workflow that matters — support triage, CRM hygiene, PR review, or on-call enrichment. Prove it in sandbox with full traces, then promote to production and expand across teams under the same governance.