Agent

Truly autonomous entities with real identity, long-term memory, and presence in your organization

Unlike simple chatbots or task runners, Agents have real identity, persistent memory, and the ability to work independently over extended time horizons.

What Makes an Agent

An Agent is more than an AI model wrapped in an API. It's an entity with:

Identity: Email, phone, Slack, calendar—real presence in organizational systems
Memory: Persistent context that grows and evolves over time
Autonomy: Ability to work independently, making decisions and taking actions
Relationships: Understanding of team structure, preferences, and history
Accountability: Clear ownership of tasks with escalation paths

import { agent, capability } from 'autonomous-agents'

const sdkExpert = agent({
  name: 'sdk-expert',

  identity: {
    displayName: 'SDK Expert',
    email: 'sdk-expert@company.com',
    slack: '@sdk-expert',
    calendar: 'sdk-expert@company.com',
  },

  personality: {
    role: 'Senior developer advocate specializing in SDK design',
    traits: ['helpful', 'thorough', 'technically precise'],
    communication: 'Clear and educational, with code examples',
  },

  capabilities: [
    answerSDKQuestions,
    reviewPullRequests,
    writeDocumentation,
    triageIssues,
  ],

  memory: {
    type: 'persistent',
    includes: ['conversations', 'preferences', 'codebase-knowledge'],
  },
})

Real Identity

Agents exist in your organization's systems as first-class entities:

// Agent receives email
const emailHandler = agent.onEmail(async (email) => {
  if (email.subject.includes('SDK question')) {
    const response = await agent.capabilities.answerSDKQuestions({
      question: email.body,
      context: await agent.memory.getConversation(email.from),
    })
    await agent.reply(email, response)
  }
})

// Agent joins Slack channels
await agent.slack.join(['#sdk-support', '#developer-questions'])

// Agent responds to mentions
agent.slack.onMention(async (message) => {
  const response = await agent.think(message.text)
  await agent.slack.reply(message, response)
})

// Agent has calendar for scheduling
await agent.calendar.createMeeting({
  title: 'SDK Office Hours',
  recurrence: 'weekly',
  duration: '1h',
})

Long-Term Memory

Agents build knowledge over time:

const memory = agent.memory

// Remember facts about users
await memory.remember({
  type: 'user-preference',
  user: 'alice@company.com',
  fact: 'Prefers TypeScript examples over JavaScript',
  confidence: 0.95,
})

// Store learned information
await memory.learn({
  type: 'codebase-pattern',
  pattern: 'Error handling uses Result type, not exceptions',
  source: 'code-review-feedback',
})

// Recall relevant context
const context = await memory.recall({
  query: 'How does Alice prefer to receive code examples?',
  types: ['user-preference', 'conversation-history'],
  limit: 10,
})

Memory Types

Episodic: Specific interactions and events
Semantic: Learned facts and knowledge
Procedural: How to perform tasks
Working: Current task context

True Autonomy

Agents work independently, making decisions without constant supervision:

const autonomousAgent = agent({
  name: 'support-agent',

  autonomy: {
    // What the agent can do without asking
    canDo: [
      'answer-common-questions',
      'look-up-documentation',
      'create-tickets',
    ],

    // When to escalate to humans
    escalateTo: 'support-team',
    escalateWhen: [
      'confidence < 0.7',
      'customer-sentiment = angry',
      'topic = billing',
      'request = refund > $100',
    ],

    // Learning from feedback
    learnFrom: ['human-corrections', 'outcome-feedback'],
  },
})

Decision Making

// Agent decides how to handle a request
const decision = await agent.decide({
  situation: 'Customer asking about enterprise pricing',
  options: [
    { action: 'answer-directly', confidence: 0.3 },
    { action: 'escalate-to-sales', confidence: 0.9 },
    { action: 'schedule-call', confidence: 0.7 },
  ],
})

// Agent takes action based on decision
await agent.execute(decision.chosen)

Working with Teams

Agents understand organizational structure:

const agent = agent({
  name: 'project-coordinator',

  team: {
    reports_to: 'engineering-manager',
    collaborates_with: ['frontend-team', 'backend-team', 'design-team'],
    can_assign_to: ['junior-developers'],
  },

  workflows: [
    dailyStandup,
    sprintPlanning,
    codeReviewAssignment,
  ],
})

// Agent coordinates work across team
await agent.coordinate({
  task: 'Implement new auth flow',
  assignees: await agent.findBestMatch({
    skills: ['react', 'oauth'],
    availability: 'this-sprint',
  }),
})

Agent Lifecycle

// Create and deploy agent
const myAgent = await agent.deploy()

// Agent is now active and responding
console.log(myAgent.status) // 'active'

// Monitor agent activity
myAgent.on('action', (action) => {
  console.log(`Agent performed: ${action.type}`)
})

// Review agent decisions
const decisions = await myAgent.getDecisionLog({
  since: lastWeek,
  includeReasoning: true,
})

// Provide feedback
await myAgent.feedback({
  decisionId: 'dec_abc123',
  outcome: 'incorrect',
  correction: 'Should have escalated this billing question',
})

// Agent learns from feedback
await myAgent.learn()

Guardrails

Agents operate within defined boundaries:

const agent = agent({
  name: 'secure-agent',

  guardrails: {
    // Rate limits
    maxActionsPerHour: 100,
    maxEmailsPerDay: 50,

    // Content policies
    neverMention: ['competitor-names', 'internal-financials'],
    alwaysInclude: ['disclaimer-for-legal-advice'],

    // Approval requirements
    requireApprovalFor: [
      { action: 'send-email', when: 'external-recipient' },
      { action: 'create-ticket', when: 'priority = critical' },
    ],
  },
})

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