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Agent architecture & orchestration
Design multi-agent topologies — supervisor, worker, critic, planner — with clear contracts between roles. Tool use, memory, and message protocols built for reliability under real load.
Autonomous agents that plan, decide, and act across long horizons. We build the reasoning loops, tool orchestration, evaluation harnesses, and safety scaffolding that turn a foundation model into a system you can deploy and trust.
Most of the AI that ships today is a thin wrapper around a chat completion. The frontier — the work that companies like Google, Anthropic, OpenAI, and Microsoft are racing toward — is agentic AI: systems that hold goals, reason about state, choose tools, and act over hours, days, or longer.
That work is fundamentally a systems-engineering problem, not a prompting problem. It needs state machines, evaluation pipelines, observability, and safety rails — built with the same rigor that real software has been built with for decades.
We design and build agentic systems that survive contact with reality: production traffic, adversarial inputs, partial failures, and the long tail of edge cases that benchmarks never cover.
Design multi-agent topologies — supervisor, worker, critic, planner — with clear contracts between roles. Tool use, memory, and message protocols built for reliability under real load.
Implement plan-and-execute, ReAct, tree-of-thought, and custom reasoning patterns. Long-horizon task decomposition with robust checkpointing and recovery.
Wire agents into your APIs, databases, code repositories, and operational tooling. Schema design, error handling, retry policies, and rate-limit-aware execution.
RAG pipelines with hybrid search, re-ranking, and grounded citations. Vector stores, semantic chunking, and freshness pipelines that keep knowledge current.
Build domain-specific eval suites — unit, integration, and adversarial. Track regression across models, prompts, and tool versions with statistical rigor.
Constitutional layers, output validators, red-teaming harnesses, and scoped permissions. We treat safety as architecture, not as an afterthought filter.
Model routing, prompt caching, speculative decoding, and quantization strategies. Get production-grade response times at sustainable token cost.
Trace every reasoning step, tool call, and decision. Replay, diff, and root-cause analysis across thousands of agent runs.
A practical, opinionated stack — chosen for production reliability, not novelty. We add to it carefully, and we share what we learn.
| Engagement model | Embedded engineering team, 6–24 weeksIncludes architecture, build, eval harness, hand-off |
|---|---|
| Typical deliverables | Production agent with eval suite, runbooks, dashboardsOwned by you on day one |
| Quality bar | Production traffic, < 1s p50 latency, > 99.5% task successTracked on a public-to-team scorecard |
| Hand-off & ownership | Full code, docs, infra-as-codeWe optionally stay on retainer for two cycles |
We work with a small number of partners each year. The right first step is usually a conversation. Or look at how we validate.
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