Agent Runtime

Agent Execution Model

Agents run in isolated runtime contexts with explicit tool access controls and policy checks per action. Each agent has a defined scope, input/output contract, and resource quota.

Agent Lifecycle

1. Registration: Agent is registered with the control plane with its capabilities and resource requirements
2. Scheduling: Runtime scheduler assigns agent to execution slot based on priority and available resources
3. Context Loading: Agent receives context window with relevant knowledge, previous execution history, and current task
4. Execution: Agent runs autonomously, invoking tools and making decisions within permission boundaries
5. Completion: Agent emits results, logs, and traces before context is archived

Context Management

Each agent maintains an isolated context window with:

• Task specification and success criteria
• Relevant knowledge from vector database
• Historical execution patterns and outcomes
• Available tools and their schemas
• Resource quotas (tokens, time, memory)

Tool Invocation

Agents interact with the platform through a controlled set of tools:

interface Tool {
  name: string;
  description: string;
  parameters: JSONSchema;
  permissions: string[];
  rateLimit: { calls: number; window: string };
}

// Example: Discovery Agent invoking API scan tool
await runtime.invokeTool({
  tool: "scan-api-endpoints",
  parameters: { baseUrl: "https://api.example.com" },
  timeout: 30000
});

Resource Quotas

Each agent type has configurable quotas to prevent runaway execution:

quotas:
  maxTokens: 100000        # LLM token budget
  maxExecutionTime: 300s   # Wall-clock timeout
  maxMemory: 512Mi         # Memory limit
  maxToolCalls: 50         # Tool invocation limit
  concurrency: 3           # Parallel tool calls

Observability

Runtime snapshots persist reasoning traces, execution logs, and step-level decision metadata:

• Every agent decision is logged with reasoning explanation
• Tool calls are traced with inputs, outputs, and latency
• Context snapshots enable replay and debugging
• Metrics exported to Prometheus for monitoring

Example: Test Planning Agent

# Agent receives task
task:
  type: "generate-test-plan"
  changeSet:
    - file: "src/checkout/payment.ts"
      impact: "high"

# Agent reasoning trace
Step 1: Retrieve knowledge about payment workflows
Step 2: Identify test coverage gaps for changed files
Step 3: Generate prioritized test cases
Step 4: Validate plan against policy constraints
Step 5: Emit test plan to execution queue

# Output
testPlan:
  priority: "high"
  tests:
    - name: "Payment flow with credit card"
      type: "ui"
      risk: "high"
    - name: "Payment API contract validation"
      type: "api"
      risk: "medium"