Full Lifecycle Agent Management
From creation to production in one platform. Configure agents with bound models, tools, and instructions. Test quality with quantitative benchmarks. Publish to your organization marketplace. The Skill System cuts per-conversation instruction cost by ~80%.
Agent Lifecycle
Create
Define agent identity, bind model, set execution mode
Configure
Attach skills, connectors, knowledge bases, and sub-agents
Test
Run evaluation datasets, measure accuracy, latency, and cost
Publish
Submit for review, approved agents appear in organization market
Monitor
Track usage, token consumption, and quality metrics
Agent Management
Create, configure, and publish agents with fine-grained control. Bind specific models, tools, connectors, and knowledge bases to each agent. Choose per-agent execution mode (Standard ReAct or Planner DAG) and set temperature for determinism control. Toggle the discoverable flag to enable LLM auto-discovery by other agents.
"instructions": "Long system prompt...",
"examples": [...],
"schemas": [...]
}
> Loading full context on demand...
Skill System
Progressive instruction loading that dramatically reduces token consumption. Skills — SOPs, scripts, domain knowledge — are referenced in the system prompt as lightweight stubs (~15 tokens each). Agents call read_skill(name) to load full content only when needed.
Traditional approach: stuff all instructions into every conversation. Skill System: load only what the agent actually needs, when it needs it.
Evaluation Center
Quantitative agent quality benchmarking before production deployment. Build test datasets with prompts, expected behaviors, and assertions. Run evaluations in parallel with LLM-graded scoring. Review per-case pass/fail, latency, and token consumption results.
Agent Auto-Discovery & Sub-Agent Binding
Enable specialist delegation without multi-agent chaos. Mark agents as discoverable, define sub_agent_ids whitelists, and use CallAgentTool to delegate tasks to the right specialist. One agent can invoke another as a tool — controlled, auditable, and intentional.
Three Execution Modes
Choose the right execution strategy for each task. Standard mode reasons step by step. Planner mode decomposes into a dependency graph and runs steps in parallel. Auto-Routing uses a fast LLM to classify queries and route to the optimal mode automatically.
| Standard (ReAct) | Planner (DAG) | Auto-Routing | |
|---|---|---|---|
| How it works | Think-act loop | Plan then parallel execute | Classify then route |
| Best for | Judgment & exploration | Multi-step workflows | Mixed workloads |
| Parallelism | Sequential | Parallel on independent steps | Depends on routed mode |
| Self-correction | Error recovery | Auto re-planning (up to 3 rounds) | Inherited from routed mode |
Enterprise
Need private deployment, custom connectors, or professional support? Our team is ready to help you scale your AI transformation.