Agentic engineering systems
Get more engineering out of your AI stack.
We help engineering teams ship faster, reduce rework, and lower AI costs by improving the systems around their coding agents.
Task, code, docs, and team rules converge into relevant context. Context guides an agent workflow, with tests and human review leading to a review-ready change. Feedback informs the next run’s context and workflow configuration.
Context. Workflows. Evaluation. Built around your stack.
A better way through01 / The engineering challenge
Better models are only part of the equation.
The opportunity is not just generating more code. It is making the path from task to trusted change work better.
We work on the system around the model.
- 01
Context gets lost.
Agents can miss the conventions, decisions, and dependencies your engineers take for granted.
- 02
Work gets repeated.
Fragile handoffs, unclear checks, and poorly scoped tasks can turn apparent speed into review and rework.
- 03
Spend is hard to explain.
More tokens and more activity do not tell you whether useful engineering work is getting done.
02 / The system around the model
Make the whole
workflow work.
We connect the pieces that determine whether agents produce useful, reviewable work—and what that work costs.
01 / Context
Give agents the context that matters.
Structure repository knowledge, instructions, retrieval, and memory so agents can find relevant information without carrying everything into every task.
- Knowledge sources
- Retrieval
- Context assembly
- Freshness
What we build
Context map, retrieval changes, and maintainable agent instructions.
Repository, documentation, and decision sources feed retrieval. Relevant material is selected into a scoped context packet with its provenance; other material stays in the source. Freshness checks keep the packet current.
02 / Workflows
Turn one-off runs into repeatable workflows.
Design task boundaries, tools, handoffs, checkpoints, and recovery paths around the way your team actually ships software.
- Task scope
- Tools
- Checkpoints
- Recovery
What we build
A working workflow with clear checks, ownership, and failure handling.
A task moves through plan, implement, and verify. Failed verification returns to implementation through a repair loop. Verified work crosses a distinct human approval boundary for human review. There is no automatic production deployment.
03 / Evaluation
Know whether the changes help.
Test alternatives on representative engineering work. Track quality, human intervention, latency, and cost—not just agent activity.
- Representative tasks
- Checks
- Traces
- Comparison
What we build
An evaluation set, a comparison method, and a clear account of tradeoffs.
One fixed set of representative tasks branches into a baseline and a candidate. Both are compared against the same review criteria: quality, human effort, latency, and cost. No winning result is assumed.
04 / Cost
Spend capability where it earns its keep.
Examine model selection, routing, context size, retries, and caching against the quality requirements of each task.
- Routing
- Context budget
- Retries
- Cost per outcome
What we build
A tested routing and cost-control policy, with explicit quality constraints.
Tasks with different needs are routed through routine, complex, or escalation paths. All three paths converge on the same acceptance checks. Routing is constrained by required quality, not just model price.
03 / How we work
Improve one real workflow.
Then expand.
Start with a concrete bottleneck. Establish a baseline, implement a focused change, and evaluate whether it is worth taking further.
- 01
Understand the system.
Map the tools, repositories, context sources, checks, and handoffs around a representative workflow. Agree on what improvement would mean.
OutputA baseline and a focused problem statement.
- 02
Build the intervention.
Implement a targeted change with your team: better context, a more reliable workflow, stronger evaluation, or more deliberate model routing.
OutputWorking code or configuration—not just recommendations.
- 03
Evaluate and hand over.
Compare the result with the baseline, document tradeoffs, and make the implementation understandable and maintainable.
OutputEvidence, documentation, and a clear next decision.
04 / Evidence over activity
Measure the work.
Not the hype.
We agree on measures that connect agent behavior to engineering outcomes. The goal is not maximum automation at any cost. It is useful progress with acceptable quality and effort.
- Delivery
- Elapsed time for comparable work, including waiting and rework
- Quality
- Acceptance checks, regressions, and review findings
- Human effort
- Corrections, interventions, and review time
- Cost
- Model and tool spend per accepted task, alongside human effort
Measures depend on the workflow and the quality of available data.
05 / A focused engineering partner
A specialist partner.
Not another platform to buy.
Peashoot works with engineering teams on the context, workflows, and feedback loops around coding agents. We start from your tools and constraints, then build and test changes against real work.
Built around your stack.
Evidence before expansion.
Engineers stay in control.
The people behind Peashoot
Brendan Duhamel
Software entrepreneur who has built and operated technology businesses internationally.
Cormac Duhamel
Software engineer working on agentic development at Chess.com.
Let’s find a better way through
Where is your engineering workflow getting stuck?
Bring a slow workflow, an unreliable agent, or an AI bill that is hard to explain. Let’s talk about what you want to improve.
Start a conversation