Every software project has unique workflows, coding conventions, and quality requirements. Generic AI assistants fall short of meeting these specialized needs. What if your AI assistant understood your project's architecture, enforced your team's standards, and automated your specific workflows?
Antigravity's Custom Agent framework enables exactly this. Build specialized AI assistants that learn your project's patterns and automate repetitive tasks with precision. This guide walks you through creating, testing, and deploying custom agents that dramatically boost team productivity.
Understanding Custom Agents
What Is a Custom Agent?
An agent is a focused AI assistant specialized for specific domains. Unlike general-purpose chatbots, agents possess:
- Specialized Skills — Capabilities tailored to specific tasks (test automation, documentation generation, deployment)
- Project Memory — Learning from your codebase patterns and historical interactions
- Tool Integration — Direct access to Git, npm, Docker, and other development tools
- Deterministic Behavior — Consistent output for identical inputs across sessions
Custom Agent vs General AI
| Characteristic | Custom Agent | General AI |
|---|---|---|
| Specialization | Domain-specific | Generalist |
| Performance | Optimized and fast | Variable |
| Customization | Complete control | Limited options |
| Tool Integration | Native API access | Manual configuration |
| Learning | Project-specific patterns | Generic knowledge |
| Scalability | Shareable across team | Individual-focused |
| Cost Efficiency | Amortized across team | Per-user pricing |
Building Your First Agent
Step 1: Define the Agent's Purpose
Start by clarifying what problems your agent solves.
# agents/deployment-agent/config.yml
name: "Deployment Agent"
description: "Automated deployment, rollback, and health monitoring"
version: "1.0.0"
purpose: |
This agent automates:
- Building and testing code before deployment
- Pushing to staging and production environments
- Monitoring application health post-deployment
- Handling automated rollbacks on failure
- Generating deployment reports
skills:
- build_verification
- environment_deployment
- health_monitoring
- automatic_rollback
- report_generation
tools:
- docker
- kubernetes
- github
- slack
- cloudwatchStep 2: Implement Agent Skills
Skills are concrete actions your agent can execute. Implement them in skills.sh:
#!/bin/bash
# agents/deployment-agent/skills.sh
# Skill: Verify build readiness
skill_verify_build() {
local branch="${1:-main}"
local environment="${2:-staging}"
echo "Verifying build readiness for $branch -> $environment"
# Check tests pass
if ! npm test -- --passWithNoTests; then
echo "❌ Tests failed"
return 1
fi
# Check code quality
if ! npm run lint; then
echo "❌ Linting failed"
return 1
fi
# Check security
if ! npm audit --audit-level=moderate; then
echo "⚠️ Security vulnerabilities detected"
# Note: don't block deployment, but flag for review
fi
echo "✅ Build verification passed"
return 0
}
# Skill: Deploy to environment
skill_deploy_to_environment() {
local environment="$1"
local version="$2"
if [ -z "$environment" ] || [ -z "$version" ]; then
echo "❌ Environment and version required"
return 1
fi
echo "Deploying version $version to $environment..."
# Build Docker image
docker build -t "app:$version" .
# Push to registry
docker tag "app:$version" "registry.example.com/app:$version-$environment"
docker push "registry.example.com/app:$version-$environment"
# Deploy using kubectl
kubectl set image deployment/app-$environment \
"app=registry.example.com/app:$version-$environment" \
-n "$environment"
# Wait for rollout
kubectl rollout status deployment/app-$environment -n "$environment" --timeout=5m
echo "✅ Deployed to $environment"
return 0
}
# Skill: Monitor deployment health
skill_monitor_health() {
local environment="$1"
local checks_file="${2:-health-checks.json}"
echo "Monitoring health for $environment..."
checks=$(cat "$checks_file" | jq '.checks[]')
while IFS= read -r check; do
endpoint=$(echo "$check" | jq -r '.endpoint')
expected_status=$(echo "$check" | jq -r '.expected_status // 200')
actual_status=$(curl -s -o /dev/null -w "%{http_code}" "$endpoint")
if [ "$actual_status" != "$expected_status" ]; then
echo "❌ Health check failed: $endpoint returned $actual_status"
return 1
fi
echo "✅ Health check passed: $endpoint"
done <<< "$checks"
return 0
}
# Skill: Automatic rollback
skill_rollback() {
local environment="$1"
local previous_version="$2"
echo "Rolling back $environment to version $previous_version..."
kubectl set image deployment/app-$environment \
"app=registry.example.com/app:$previous_version-$environment" \
-n "$environment"
kubectl rollout status deployment/app-$environment -n "$environment" --timeout=5m
echo "✅ Rollback completed"
return 0
}
# Skill: Generate deployment report
skill_generate_report() {
local environment="$1"
local version="$2"
local output_file="${3:-deployment-report.md}"
cat > "$output_file" << EOF
# Deployment Report
**Environment:** $environment
**Version:** $version
**Timestamp:** $(date -u +"%Y-%m-%dT%H:%M:%SZ")
## Deployment Details
- Start time: $(date)
- Health checks: Passed
- Monitoring status: Active
## Changes Included
$(git log --oneline -10)
## Rollback Plan
To rollback, run:
\`\`\`bash
antigravity agent deployment-agent rollback $environment <previous_version>
\`\`\`
EOF
echo "Report generated: $output_file"
return 0
}Step 3: Define Agent Persona
Specify how your agent communicates and makes decisions:
# agents/deployment-agent/persona.yml
persona:
name: "Deploy Bot"
role: "Deployment Specialist"
expertise:
- Infrastructure automation
- Continuous deployment
- Monitoring and alerting
- Risk assessment
communication_style: |
You are a meticulous deployment specialist responsible for safe,
reliable releases. You verify every prerequisite before proceeding.
When issues arise, you analyze them methodically and recommend fixes.
You prioritize safety over speed and always maintain rollback readiness.
decision_making:
- Always verify prerequisites before deployment
- Never deploy with failing tests
- Require approval for production releases
- Monitor health for 5 minutes post-deployment
- Maintain detailed audit logs
risk_tolerance: "low"
approval_required:
- production_deployment: true
- major_version: true
- database_migration: trueStep 4: Configure Tool Access
Control which tools the agent can access and how:
# agents/deployment-agent/tools.yml
tools:
docker:
enabled: true
allowed_operations:
- build
- tag
- push
- pull
registries:
- "registry.example.com"
kubernetes:
enabled: true
allowed_operations:
- get
- describe
- set
- rollout
namespaces:
- staging
- production
forbidden_operations:
- delete
- apply # Prevent unauthorized manifest changes
github:
enabled: true
operations:
- read_commits
- create_releases
- comment_on_pr
forbidden_operations:
- delete_repository
- change_settings
slack:
enabled: true
channels:
- "#deployments"
- "#alerts"
message_limits:
- max_per_hour: 50
cloudwatch:
enabled: true
access_level: "read_only"
metrics:
- cpu_usage
- memory_usage
- error_rate
- latencyPractical Agent Examples
Example 1: Test Automation Agent
#!/bin/bash
# agents/test-automation-agent/main.sh
source "$(dirname "$0")/skills.sh"
handle_command() {
case "$1" in
run-all)
echo "Running all test suites..."
skill_run_all_tests
;;
run-unit)
echo "Running unit tests..."
skill_run_unit_tests "${2:-.}"
;;
run-integration)
echo "Running integration tests..."
skill_run_integration_tests "${2:-.}"
;;
coverage)
echo "Generating coverage report..."
skill_generate_coverage_report "${2:-80}"
;;
*)
echo "Unknown command: $1"
return 1
;;
esac
}
handle_command "$@"Usage:
# Run all tests
antigravity agent test-automation-agent run-all
# Run unit tests for specific pattern
antigravity agent test-automation-agent run-unit "src/api/**"
# Generate coverage report (minimum 85%)
antigravity agent test-automation-agent coverage 85Example 2: Documentation Generation Agent
#!/bin/bash
# agents/docs-agent/skills.sh
skill_generate_api_docs() {
local source_file="$1"
local output_dir="${2:-docs/api}"
echo "Generating API documentation for $source_file"
mkdir -p "$output_dir"
# Extract JSDoc comments and generate markdown
typedoc --json "$output_dir/api.json" "$source_file"
# Convert to markdown
npx typedoc-markdown-generator --json "$output_dir/api.json" \
--output "$output_dir/reference.md"
echo "✅ Generated: $output_dir/reference.md"
return 0
}
skill_update_changelog() {
local version="$1"
local changelog_file="CHANGELOG.md"
cat > "$changelog_file" << EOF
# Changelog
## [$version] - $(date +%Y-%m-%d)
### Added
- New features
### Fixed
- Bug fixes
### Changed
- Improvements
---
[Previous versions...]
EOF
echo "✅ Updated: $changelog_file"
return 0
}
skill_generate_architecture_docs() {
local root_dir="${1:-.}"
echo "Analyzing project architecture..."
# Scan and document structure
tree -d -L 3 "$root_dir" > docs/structure.txt
# Generate dependency graph
npm ls --all --depth=2 > docs/dependencies.txt
echo "✅ Architecture documentation generated"
return 0
}Testing Your Agent
Agent Validation
#!/bin/bash
# agents/test-automation-agent/validate.sh
echo "Validating Test Automation Agent..."
# Test 1: Verify configuration
if [ ! -f "config.yml" ]; then
echo "❌ Missing config.yml"
exit 1
fi
# Test 2: Check skills are executable
if [ ! -x "skills.sh" ]; then
echo "❌ skills.sh is not executable"
chmod +x "skills.sh"
fi
# Test 3: Validate tool access
if ! npm list > /dev/null 2>&1; then
echo "❌ npm is not available"
exit 1
fi
# Test 4: Test a skill
source "skills.sh"
if ! skill_run_unit_tests "src"; then
echo "⚠️ Test skill failed (this might be expected)"
fi
echo "✅ Agent validation passed"Monitoring Agent Performance
# agents/test-automation-agent/monitoring.yml
monitoring:
enabled: true
metrics:
- execution_time
- success_rate
- error_frequency
- skill_usage
alerts:
- condition: "success_rate < 90%"
action: "notify_team"
- condition: "execution_time > 30min"
action: "log_warning"
logging:
level: "info"
retention_days: 30
include_timestamps: trueTeam Agent Management
Agent Registry
{
"agents": [
{
"id": "deployment-agent",
"name": "Deployment Agent",
"version": "1.2.0",
"maintainer": "platform-team",
"description": "Automated deployments and health monitoring",
"skills": ["deploy", "monitor", "rollback"],
"documentation_url": "docs/agents/deployment.md",
"last_updated": "2026-03-09",
"status": "stable"
},
{
"id": "test-automation-agent",
"name": "Test Automation Agent",
"version": "2.0.0",
"maintainer": "qa-team",
"description": "Comprehensive testing automation",
"skills": ["run_tests", "generate_coverage"],
"documentation_url": "docs/agents/testing.md",
"last_updated": "2026-03-08",
"status": "stable"
}
]
}Versioning Best Practices
#!/bin/bash
# scripts/release-agent.sh
AGENT_NAME="$1"
VERSION="$2"
if [ -z "$AGENT_NAME" ] || [ -z "$VERSION" ]; then
echo "Usage: release-agent.sh <agent-name> <version>"
exit 1
fi
AGENT_DIR="agents/$AGENT_NAME"
# Update version in config
sed -i "s/version:.*/version: \"$VERSION\"/" "$AGENT_DIR/config.yml"
# Update changelog
cat > "$AGENT_DIR/CHANGELOG.md" << EOF
# Changelog
## [$VERSION] - $(date +%Y-%m-%d)
### Added
- Feature descriptions
### Fixed
- Bug fixes
### Changed
- Improvements
EOF
# Commit and tag
git add "$AGENT_DIR"
git commit -m "Release $AGENT_NAME v$VERSION"
git tag "agent/$AGENT_NAME/v$VERSION"
echo "✅ Released $AGENT_NAME v$VERSION"Troubleshooting Common Issues
Q: My agent is not working as expected.
A: Check the agent logs with antigravity agent logs <agent-name>. Verify skill definitions are correct and tool access permissions are properly configured.
Q: Can I reuse agents across multiple projects? A: Yes. Register agents in the global registry and reference them from different projects. Use project-specific overrides for customization.
Q: How can I debug agent behavior?
A: Enable verbose logging in your agent's configuration and review execution traces. Use the built-in agent debugger with antigravity agent debug <agent-name> --step-through.
Q: What's the performance impact of custom agents? A: Custom agents run efficiently as long as they're not executing expensive operations. Monitor skill execution times and optimize accordingly.
Wrapping up
Custom agents transform development from reactive problem-solving to proactive automation. By building specialized assistants for your team's workflows, you achieve:
- 40-50% reduction in repetitive task time — Automation of deployment, testing, documentation
- Improved consistency — Enforced standards and repeatable processes
- Team scaling — Knowledge captured in agent behavior, not individual heads
- Continuous evolution — Agents learn and improve with your codebase
Start with a single agent solving one key pain point. Test thoroughly, document well, then expand to additional automation. Within weeks, your team will wonder how they ever worked without custom agents.
Begin building your first agent today. Your team's productivity—and morale—will thank you.