Make AI agents part of the engineering operating model — not an exception to it.
Introduce AI agents into engineering work with the process context, rules, authority boundaries, human checkpoints, Definition of Done, and provenance they need to operate responsibly.
PEDCO combines process engineering, assurance expertise, and the Applied SAFe® Platform to help organizations move from isolated AI experiments to governed agentic engineering.
Adding an agent means adding a new executor.
AI coding agents, requirements agents, test agents, documentation agents, and other engineering agents can increasingly perform work rather than merely assist with it.
But an agent that understands the repository or ticket does not automatically understand the process around it.
The result is not a second process for AI. It is one operating model with different executors and clearly defined authority.
- Which process applies
- Which activities are mandatory in this context
- What work products must be created or updated
- What “done” means for the task
- Which rules must never be bypassed
- Which tools it may use
- Which decisions it may make autonomously
- Where a human must review or approve
- What execution evidence must be retained
Assess → Design → Implement → Assure.
The stages can be used independently. An organization may start with a readiness assessment, engage PEDCO only for control design, or ask for assurance of an agent workflow that already exists.
- Assess
- Where should agents be used?
- What risks and constraints apply?
- Design
- What process applies?
- What skills, rules, and DoD are required?
- What may the agent decide?
- Where must humans intervene?
- What provenance must be retained?
- Implement
- Connect the agent to the process and tools.
- Implement machine-checkable controls.
- Run a controlled pilot.
- Assure
- Did the agent follow the effective process?
- Were boundaries and checkpoints respected?
- Is the provenance complete?
- Can the outputs and tools be trusted sufficiently?
Eleven services across the four stages.
Assess
Decide where agents belong before deciding which agent to deploy.
Agentic Engineering Readiness Assessment
The assessment evaluates selected engineering processes, activities, decisions, tools, and assurance requirements to identify viable agent use cases and the prerequisites for responsible implementation.
Benefits- Decide where agents belong before deciding which agent to deploy.
- Separate the activities an agent can execute from the decisions that must remain human.
- Know the process, tooling and assurance prerequisites before a pilot starts.
Design
Make the operating model executable — and its boundaries explicit.
Agent Operating Model Design
Which role does the agent perform, which human role owns its work, what may it initiate, what outputs may it modify, how does it interact with people and other agents, and who is accountable when it reaches a decision boundary?
Benefits- Avoid introducing agents as an unmanaged side process.
- Clarify ownership and accountability.
- Reduce ambiguity between agent actions and human responsibilities.
- Make agent behavior easier to scale across workflows and teams.
- Create the foundation for skills, rules, checkpoints, and assurance.
Skills, Rules & Hooks Engineering
Skills describe reusable capabilities. Rules capture contextual constraints and instructions. Hooks are deterministic checks or actions triggered at defined points in the workflow. Together they express process intent in a form an agent can act on.
Benefits- Derive agent behavior from the governed process rather than ad-hoc prompts.
- Keep instructions and controls versioned with the operating model.
- Reuse skills across workflows and teams.
- Make constraints explicit rather than implicit in a prompt.
Definition-of-Done Engineering
It may still require impact analysis, updated traceability, review evidence, documentation, safety approval, or another context-specific condition. Those expectations come from the process, not from the agent's own judgment.
Benefits- Stop work being called complete when process conditions remain open.
- Make completion criteria context-specific rather than generic.
- Enforce automatically what can be checked automatically.
- Keep the judgment-based conditions visibly with people.
Authority & Human Checkpoint Design
Authority & Human Checkpoint Design defines those boundaries explicitly and connects them to the effective process, risk context, and organizational accountability.
Benefits- Make the limits of agent autonomy explicit rather than emergent.
- Keep accountable decisions with accountable people.
- Give reviewers the evidence a decision actually needs.
- Let autonomy widen safely as confidence grows.
Provenance & Assurance Design
When an AI agent contributes to engineering work, teams may later need to understand which agent acted, under which process version, with which tools, under which controls, and with which human decisions in between.
Benefits- Reconstruct how a result was reached, not just what it was.
- Keep evidence proportionate to the assurance need.
- Make the record readable by a reviewer, not only by a machine.
- Give audits and investigations something to work from.
Implement
Connect the agent to the process, the tools, and the controls.
Agent Pilot Implementation
One use case with clear process expectations, inputs, outputs, tools, authority, and success criteria tells you far more than a broad rollout — because everything around the agent is observable.
Benefits- Validate the operating model, the controls and the human interaction — not only the agent.
- Learn from a bounded workflow where everything around the agent is observable.
- End with a decision to scale, constrain, redesign or stop, backed by real execution.
Agent Integration
Agent Integration connects the Applied SAFe® operating model and the enterprise engineering environment with the selected agent platform through supported mechanisms.
Benefits- Give the agent the process context for the work actually being performed.
- Avoid re-stating the operating model in every agent configuration.
- Make available tools reflect the designed authority model.
- Validate the integration against representative scenarios rather than assuming it.
Deterministic Control Implementation
Tests must pass. A required artifact must exist. Traceability must be updated. A review must be complete. A mandatory approval must be present. Conditions like these have unambiguous evidence, and a control can verify them without interpretation.
Benefits- Stop objective conditions depending on an agent reading guidance correctly.
- Make failure behavior an explicit design decision.
- Reserve human attention for the judgment calls.
- Test the paths that matter when something is missing.
Assure
Verify how the agent worked — not only what it produced.
Agent Execution Assurance
Agent Execution Assurance evaluates selected agent execution against the effective process and the controls designed for that workflow — process adherence, rule compliance, authority, checkpoints, Definition of Done, control behavior, and exceptions.
Benefits- Show that execution stayed inside the process, the authority model and the Definition of Done.
- Distinguish an agent failure from an integration failure, a control weakness or an operating-model issue.
- Prioritize remediation on evidence rather than on impression.
Agent Provenance Review
The review examines whether selected agent runs can be reconstructed sufficiently to understand the process context, actions, tools, controls, human decisions, and outputs involved.
Benefits- Find out before an audit whether the record actually explains the run.
- Distinguish evidence that exists from evidence that is usable.
- Expose relationships that were never captured.
- Improve the provenance model from real executions.
The operating model does not just tell the agent what to do.
It defines when the work is complete, what the agent is allowed to decide, what must be checked, and where a human must remain in control.
- Applied SAFe® operating modelresolved by context into the effective process
- Skillswhat to doRuleshow to operateDefinition of Donewhat “done” means
- Authority model
- Autonomous actionwithin defined conditionsHuman checkpointreview, approval, or escalation
- Agent executiontools + hooks
- Outputs + execution evidence
- AssureProvenanceImprove
Questions we get
Is this mainly about AI coding agents?
No. Coding agents are one important use case, but the same operating-model approach can be applied to requirements, testing, documentation, compliance support, analysis, review, and other engineering activities.
Do these services apply to any process, or only to engineering?
Only to engineering. The scope is the engineering organization and the processes it executes — requirements, design, implementation, test, documentation, release, and the assurance around them. Agentic Engineering starts from the effective engineering process, so applying the same approach to a finance, HR, or service-management process would mean starting from that operating model instead.
Do agents need their own separate process?
Not by default. The objective is to use the same governed operating model as the human organization and resolve what applies to the agent in the specific context.
Does every process rule need to become an automated control?
No. Some expectations can be checked deterministically; others require judgment. The service distinguishes machine-checkable conditions from rules that remain guidance or require human review.
Can an AI agent approve its own work?
That depends on the process, risk, and assurance context. Authority & Human Checkpoint Design explicitly defines which actions may be autonomous and which decisions require a person or an independent role.
Is this only for regulated industries?
No. Governance, Definition of Done, tool boundaries, and provenance can be valuable in any engineering organization. Regulated and high-assurance environments simply place stronger requirements on evidence, independence, traceability, and control.
Does PEDCO provide the AI agent itself?
The service family is centered on the operating model, controls, integration, and assurance around engineering agents. The actual agent can be an existing customer tool, a third-party engineering agent, or an agent implemented as part of a pilot where appropriate.
Can this work with different agent platforms?
Yes. The design should remain centered on the governed process and expose relevant context and controls through supported mechanisms such as APIs, MCP, instructions, hooks, and engineering-tool integrations rather than depending on one agent product.
Give agents the process context and controls the work requires.
Whether you are exploring your first engineering-agent use case, designing authority boundaries, connecting an existing coding agent to the operating model, or assuring agent execution in a regulated environment, start with the process and risk context that actually applies.