[ddev/ai/phases]: Introduce AgenticPhase and make Phase an abstract lifecycle base

[luisorofino]

What does this PR do?

Refactors the phases layer of the AI framework to separate the lifecycle contract from the agent-driven implementation.

• Splits Phase into two classes:
  • Phase (abstract): owns the immutable lifecycle skeleton — should_process_message, process_message, on_success, on_error, checkpoint writing, memory persistence. Subclasses must implement async execute(context) -> PhaseOutcome.
  • AgenticPhase(Phase): owns the LLM pipeline — builds the agent and ReActProcess, runs tasks (run_tasks), runs the memory step (_run_memory_step), and exposes before_react / after_react hooks.
• Introduces a PhaseOutcome dataclass (memory_text, total_input_tokens, total_output_tokens, extra_checkpoint) as the single return value of execute(). The base Phase.process_message consumes it to assemble the checkpoint payload, so subclasses no longer reach into checkpoint internals.
• Adds Phase.validate_config classmethod (no-op by default) and invokes it from the orchestrator for every phase scheduled in flow:. Orphan phases (defined but not in flow:) are skipped. AgenticPhase.validate_config enforces agent is set, refers to a known agent, and that tasks is non-empty.
• Makes PhaseConfig.agent and PhaseConfig.tasks optional (with type default \"AgenticPhase\"), so non-agentic Phase subclasses can be configured without dummy agent/tasks entries.
• Moves the agent_config / anthropic_client constructor parameters off of Phase and onto AgenticPhase, where they belong. The orchestrator only passes them when instantiating an AgenticPhase subclass.
• Reorganizes tests: agent-driven behaviour now lives in test_agentic_phase.py; test_base.py covers the lifecycle contract via a _StubPhase. Shared mock helpers and fixtures (MockAgent, make_agent_phase, flow_dir, message_queue) moved into phases/conftest.py.

Motivation

Until now, Phase was both the lifecycle contract and the agent-driven implementation, which made it impossible to add a non-LLM Phase subclass (e.g. a deterministic validation phase, a phase that just reads files, etc.) without dragging along the entire agent machinery. Separating the two layers keeps the framework generic and reusable, and allows us to define non-LLM-driven phases.

Review checklist (to be filled by reviewers)

• Feature or bugfix MUST have appropriate tests (unit, integration, e2e)
• Add the `qa/skip-qa` label if the PR doesn't need to be tested during QA.
• If you need to backport this PR to another branch, you can add the `backport/` label to the PR and it will automatically open a backport PR once this one is merged

[datadog-official]

✨ Fix all issues with BitsAI or with Cursor

⚠️ Warnings

🧪 7 Tests failed

❄️ Known flaky: test_config[Metric collection disabled] from test_config.py     (Fix with Cursor)

RetryError[<Future at 0x7ff9e4f1b890 state=finished raised RetryError>]

❄️ Known flaky: test_config[Resource stats collection disabled; message_flows enabled] from test_config.py     (Fix with Cursor)

RetryError[<Future at 0x7ff9e4f1b890 state=finished raised RetryError>]

❄️ Known flaky: test_config[Resource stats collection enabled; message_flows disabled] from test_config.py     (Fix with Cursor)

RetryError[<Future at 0x7ff9e4f1b890 state=finished raised RetryError>]

View all

ℹ️ Info

No other issues found (see more)

❄️ No new flaky tests detected

🎯 Code Coverage (details)
• Patch Coverage: 98.09%
• Overall Coverage: 87.59% (-0.00%)

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[codecov]

Codecov Report

❌ Patch coverage is 98.08917% with 9 lines in your changes missing coverage. Please review.
✅ Project coverage is 90.99%. Comparing base (b288a9b) to head (36d0a4b).

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[dd-octo-sts]

Validation Report

All 20 validations passed.

Show details

Validation	Description	Status
agent-reqs	Verify check versions match the Agent requirements file	✅
ci	Validate CI configuration and Codecov settings	✅
codeowners	Validate every integration has a CODEOWNERS entry	✅
config	Validate default configuration files against spec.yaml	✅
dep	Verify dependency pins are consistent and Agent-compatible	✅
http	Validate integrations use the HTTP wrapper correctly	✅
imports	Validate check imports do not use deprecated modules	✅
integration-style	Validate check code style conventions	✅
jmx-metrics	Validate JMX metrics definition files and config	✅
labeler	Validate PR labeler config matches integration directories	✅
legacy-signature	Validate no integration uses the legacy Agent check signature	✅
license-headers	Validate Python files have proper license headers	✅
licenses	Validate third-party license attribution list	✅
metadata	Validate metadata.csv metric definitions	✅
models	Validate configuration data models match spec.yaml	✅
openmetrics	Validate OpenMetrics integrations disable the metric limit	✅
package	Validate Python package metadata and naming	✅
readmes	Validate README files have required sections	✅
saved-views	Validate saved view JSON file structure and fields	✅
version	Validate version consistency between package and changelog	✅

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