Ungoverned context
Agents load full repos, long traces, and redundant docs into every call — multiplying tokens without improving output.
Agentic Engineering
Scale agent workflows without scaling token burn.
Teams are past the demo phase. The question is how to wire agents into engineering with predictable cost, clear options, and line of sight from token spend to outcome. We help you find the cost opportunity — and build the operating layer to capture it.
Token efficiency
Options analysis
Cost observability
The problem
Agent adoption outpaced agent economics.
Coding agents, review bots, and ops assistants are live — but most teams cannot explain what they spend, why it is growing, or which workflows are worth scaling.
Tool sprawl
Overlapping assistants across teams duplicate spend and context with no shared eval or guardrail layer.
No cost attribution
Leadership sees a rising inference bill but cannot tie spend to workflows, teams, or releases.
Options and cost
Three paths — with very different cost curves.
The same agentic outcome can be reached through self-serve tooling, a platform bet, or embedded engineering. We help you compare the tradeoffs honestly — then build what fits your SDLC and cost targets.
Token efficiency
Where the cost opportunity actually lives.
Token efficiency is not a prompt hack. It is architecture — what context agents see, which models run which steps, and how you measure cost against quality before you scale.
Context design
Load only what the task needs — repo slices, traces, and policies — instead of shipping the whole codebase into every call.
Model routing
Route lightweight steps to smaller models and reserve frontier models for decisions that actually need them.
Caching and retrieval
Reuse embeddings, summaries, and tool results so agents are not re-reading the same material on every loop.
Cost observability
Attribute token spend to workflows, teams, and releases — and gate expensive paths behind evals before they reach production.
What we deliver
From cost assessment to operating discipline.
Engagements typically start with a baseline — where tokens go today, which options fit your team, and what to build first for the highest return.
Spend baseline and options map
Inventory current agent tooling, token patterns, and the build-vs-buy tradeoffs for your SDLC — with clear cost and fit criteria.
Context and routing architecture
Design what agents load, which models run which steps, and where caching and retrieval cut repeat spend.
Cost and quality dashboards
Metrics leadership can use — cost per workflow, quality per eval, and guardrails that stop runaway loops before the bill arrives.