Strategy
Connect company goals to a prioritized workflow roadmap. Agree value, risk, owners, and success measures, then review the priorities as evidence develops.
Ongoing collaboration
Assess, implement, and keep improving together.
We work alongside your team to assess its starting point, implement changes across strategy, process, and tooling, and keep improving from measured results.
Strategy, process, and platform
Improve the whole system around the work.
We agree the outcomes that matter, assess the workflows and constraints, and prioritize changes with your team. Implementation, working practices, and training move together so useful tooling can become everyday delivery.
Process
Define task intake, delegation, handoffs, review, approvals, and escalation. Prepare workflow owners and reviewers to use the new practices.
Platform
Build the environments, context, approved tools, execution, and checks needed for the chosen workflows. Make quality, failures, and cost visible.
We review the results together, improve the limiting parts of the system, and expand only when the evidence and controls justify it. Your team retains ownership of the priorities, workflows, and final decisions.
Implementation in practice
From a clear request to a reviewed result.
For a chosen software delivery workflow, we connect agent tooling to your team's approved tools, checks, and decision points. We make task status, evidence, and handoffs visible as work moves through it.
Request
Define the work, its owner, and the expected result.
Plan
Human decisionA person agrees the plan, task boundary, and context before the agent starts.
Build
Execute in a controlled environment with approved tools and access.
Check
Human decisionA person reviews the acceptance and regression results and decides whether the work meets the requirements.
Approve
Human decisionA person reviews the change and decides whether it may proceed.
Release
Human decisionFollow the team's release process with strong human control.
Learn
Use results and failures to propose tested, governed improvements.
Failed or ambiguous work stops with a clear handoff. Additional autonomy is introduced only where the evidence and controls justify it.
The capabilities behind repeatable agent work.
The technical scope follows your priorities and readiness. We select the capabilities that support the chosen workflows, with shared practices, training, and controls connected to the implementation.
Development environment standardization
Repeatable local, remote, or cloud environments with the dependencies, instructions, and permissions a workflow needs.
Context engineering
Relevant code and company information, clear source ownership, and focused tool outputs instead of unnecessary context.
Agent orchestration
Defined tasks, queues, handoffs, and parallel execution where work can be separated safely.
Agent observability
Run logs, task status, failures, and useful execution evidence so people can understand what happened.
Evaluations
Representative tasks and repeatable checks that show whether a change improves correctness or introduces regressions.
Model routing
Match model capability to task difficulty, with explicit escalation and retry limits.
Token optimization
Track model spend per successful task and reduce wasted context, repeated inputs, and unnecessary attempts.
Controls and adoption
Autonomy and human approvals
Agree what agents may do, what they must escalate, and where people review or approve the result.
Governed learning and team enablement
Use failures and feedback to test improvements. Train owners and reviewers, and keep operating guidance current.
Make it work in everyday delivery
Roll out the workflow with the people who own it.
We agree a useful scope, implement the needed platform and working practices, and prepare the team to use them. Training, task intake, verification, and reviewer capacity are planned alongside the tooling.
Your team retains ownership of the workflow, its access rules, and its release decisions. We consult and help you improve from evidence as the work becomes routine.
Strategy
Where should AI create value?
Process
How should teams work with agents?
Platform
What do agents need to deliver?
Collaboration with a measurable purpose.
These separate engagements show how shared practices, implementation, training, and evidence can improve software delivery. Each result belongs to its own scope and baseline.
Standardization
151% higher delivery velocity
Three teams with 15 developers adopted shared agent configurations, training, and verification practices.
151% higher delivery velocity across three teams.
Autonomous development
4–7× faster team delivery
Three teams with 24 developers increased total delivery of changes on a cloud platform, with parallel execution and tested changes ready for human review.
4–7× faster team delivery with parallel cloud agents.
Token optimization
53% lower token costs
Four teams with 32 developers reduced model API spend with task-based routing and a more focused agent harness.
53% lower cost per successful task.
Evaluation and benchmarking
87% agent success
Two teams with 18 developers used representative tasks and repeatable evaluation to improve verified correctness.
87% verified success on the held-out benchmark.
Start a conversation
What would you like your team to achieve with AI?
Tell us how your software team uses AI today and what you’d like to improve. We’ll discuss whether tailored training or an ongoing collaboration fits.