Developer SDK & Plug-in Framework

Programmatic Control over Autonomous Agents

Objective

Enable developers to build, test, and deploy custom autonomous agents within the UA1 protocol by exposing low-level hooks, runtime scaffolding, and secure modular skill injection pipelines.

Agents are packaged and deployed as containerized microservices running isolated execution environments, each with statically defined permissions and memory access scopes. The framework supports live skill loading, event-driven logic, and agent-to-agent communication patterns.

Architecture Summary

Component	Description
Agent Runtime Core	Lightweight, container-ready execution engine (Python or JS context)
Skill Engine	Loads custom functions, tools, conditionals, and API calls at runtime
Manifest Compiler	Converts YAML agent definition into executable, permission-aware artifact
Telemetry Layer	Streams live logs, error traces, and memory diffs for observability
State Manager	Enforces memory boundaries, version checkpoints, rollback on failure

Agent File Structure Example

bashCopierModifiermy-agent/
├── agent.yaml                # Agent manifest (meta + policy + skill bindings)
├── skills/
│   ├── pricing_bot.py        # Tool: price comparison
│   ├── alert_sender.py       # Tool: email/telegram notifications
├── memory/
│   └── init_knowledge.json   # Bootstrapped memory (optional)
├── tests/
│   └── test_agent_flow.py    # Pytest-compatible test suite
├── Dockerfile                # Runtime container
├── logs/                     # Local telemetry output

Key Features

Feature	Technical Implementation
Agent Manifest System	YAML-based schema with role, skills, auth scopes, resource constraints
Skill Modules	Python/Node modules with execute() entrypoints; hot-swappable via plugin loader
Runtime Isolation	Each agent runs in its own Docker container or Firecracker VM, memory-scoped
CLI Toolkit	Create, compile, test, deploy agents; supports versioning + local emulation
Monitoring Hooks	Custom log levels, memory deltas, error codes, and task success callbacks

Agent Manifest (YAML) Example

yamlCopierModifieragent:
  id: "shopping_analyst_v1"
  language: "python"
  description: "Compares e-commerce pricing across platforms"
  memory_policy:
    encryption: true
    max_context_window: 4096
  permissions:
    - fetch_http
    - write_to_owner_log
    - call_api("amazon", "price_scraper")
  skills:
    - skills/pricing_bot.py
    - skills/alert_sender.py

Build & Deployment Lifecycle

1. Scaffold Agent

   bashCopierModifierera-cli init my-agent --template basic-python

2. Write Skills

   • Each file must export an execute(input, context) method

   • Tools are versioned independently via semantic versioning

3. Test Locally

   bashCopierModifierera-cli test

4. Deploy

   bashCopierModifierera-cli deploy --env=production

5. Monitor

   bashCopierModifierera-cli logs --tail

6. Update or Rollback

   • Skills hot-reloadable in non-critical agents

   • Full rollback supported for production deployments

Advanced Capabilities

Capability	Details
Agent Federation	Agents can call one another’s skills across containers with auth relay
Skill Marketplace	Signed skill packages (.erskill) can be published and reused
Performance Profiling	Execution time, memory usage, failure rates available via observability tools
Simulated Training	Emulator supports complex input chains and prompt randomization