AI Agent Engineering
Governed AI work loops with LangGraph, CrewAI, HITL approval, typed outputs, traceability, checkpoint persistence, and production fault tolerance.
What happens after you submit specs
1. Context
We inspect the system, constraints, and where delivery or architecture risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory track, scoped build, or a clear signal that the work is not ready yet.
3. Next Step
If there is a fit, we define the shortest path to a useful engagement and a production-ready outcome.
Governed AI Work Loops, Not Demo Agents
Every agent workflow we deploy has a work contract: bounded objective, typed inputs and outputs, allowed tools, forbidden actions, evidence requirements, review gates, and ownership of final quality. No black boxes.
The useful unit is not “an agent.” It is a governed work loop: intake, scoped execution, evidence capture, review, delivery, feedback, and memory update.
Before You Build
Not every AI problem needs an autonomous agent. 80% of “agentic” use cases are better served by deterministic workflows or simple RAG pipelines. Our AI Strategy & Advisory practice helps enterprise teams assess suitability, design governance frameworks, and avoid costly over-engineering — before writing a line of agent code.
Typical engagement starts when
- a demo or pilot proved demand, but the system now needs state, retries, approvals, and production observability
- multiple tools or data sources have to be orchestrated under explicit boundaries instead of chained prompts
- an internal team is choosing between workflow, single-agent, and multi-agent designs and needs the decision grounded in production trade-offs
- latency, reliability, or human-review pressure is exposing weak architecture in an already-live workflow
What We Build
| Capability | What We Deliver |
|---|---|
| Multi-agent orchestration | LangGraph state machines with checkpoint persistence, fault tolerance, and human-in-the-loop approval gates |
| Single-agent RAG pipelines | Retrieval-augmented generation with self-correction, evaluation pipelines, and semantic search at scale |
| Governed work loops | End-to-end execution with scoped intake, structured outputs, evidence capture, review gates, feedback, and memory update |
| Voice workflow pilots | Meeting or phone assistants that produce reviewable artifacts under explicit disclosure, context boundaries, cost caps, and human escalation rules |
| Multi-agent competitive intelligence | Parallel agent execution with structured data extraction, priority routing, and compliance checkpoints |
Engineering Standards
Every agent deployment includes:
- Structured state management with typed checkpoints
- LangSmith observability for trace-level debugging
- HITL approval gates at critical decision points
- Pydantic-validated outputs at every agent boundary
- Fault tolerance with retry logic and dead-letter queues
- Evidence artifacts for claims, tool actions, and delivery decisions
- Clear owner and escalation boundary for final quality
Common failure patterns we fix
- synchronous model calls blocking user-facing sessions under load
- tool-call loops with no exit condition or escalation path
- context bloat from naive retrieval or prompt assembly
- no evaluation pipeline, so regressions ship silently
- retries and fallback logic missing around rate limits or transient model failures
What you leave with
- a deployed or implementation-ready agent workflow with clear state boundaries
- approval paths, failure handling, and observability designed into the system
- evaluation and rollout criteria the internal team can keep using after handoff
- proof artifacts that make the agent’s work inspectable instead of merely plausible
- architecture decisions documented well enough to extend the system without starting over
Performance
- p99 checkpoint latency: 38ms
- 800 concurrent agent sessions
- Zero unhandled failures in production
These numbers matter because they describe runtime reliability, not demo behavior. Fast checkpointing keeps retries and human approvals usable under load, and zero unhandled failures means the system stayed operable when real workflows got messy.
Best Fit
- Team already has multiple tools, approvals, or branching workflows that cannot be reduced to one deterministic path
- CTO or VP Eng needs agent orchestration with traceability, checkpoints, and production observability
- Product requires HITL gates, auditability, and failure recovery across long-running tasks
- Organization is prepared to treat agent systems as software infrastructure, not prompt experiments
- Post-POC or first-AI-feature team needs architecture that survives real traffic and changing requirements
When to Use This
| If Your Situation Is | Then We Recommend |
|---|---|
| Single data source, deterministic logic, no ambiguity | Deterministic workflow — not an agent |
| One LLM call with structured output, no tool use | Simple RAG pipeline with Pydantic validation |
| Multiple tools, conditional branching, human approval needed | Single LangGraph agent with HITL gates |
| The use case is a meeting assistant, phone intake, or call-artifact workflow | AI Meeting Readiness Review — prove the workflow boundary before production build |
| Parallel execution across independent data sources | CrewAI multi-agent with specialist delegation |
| Adversarial review, cross-vendor debate, quality gates | Multi-model adversarial pipeline (Axion pattern) |
| Not sure whether you need agents at all | AI Strategy Advisory — assess first, build second |
| System is already live and the main problem is reliability, retrieval, or rollout strain | Stabilization Sprint — corrective engineering before broader build scope expands |
| Architecture is already settled and the main need is execution capacity with senior oversight | Embedded Delivery Pod — reserve a principal-led build cell around the workstream |
Specialist Capabilities
| Capability | Focus |
|---|---|
| CrewAI Agent Engineering | Hierarchical agent teams, specialist delegation, multi-agent orchestration |
| LangChain & LangGraph Engineering | Stateful agent workflows, self-correcting pipelines, LangSmith observability |
| RAG & Retrieval Engineering | Hybrid retrieval pipelines, vector + graph + SQL, evaluation frameworks |
| AI Strategy & Advisory | Agentic suitability assessment, architecture design, enterprise advisory engagements |
| Agent Governance & Compliance | Tool permission design, HITL checkpoint policies, audit trail architecture, compliance frameworks |
| Stabilization Sprint | Bounded rescue work when an active system needs corrective engineering before the next build phase |
| Embedded Delivery Pod | Principal-led reserved capacity when the architecture is clear and execution needs a dedicated cell |
| Temporal Workflow Engineering | Durable execution, failure recovery, and long-running orchestration for agent systems |
| AI Observability Engineering | LangSmith, OpenTelemetry, cost attribution, and compliance audit trails |
| AI Meeting Readiness Review | Feasibility review for meeting assistants, phone intake, and voice-driven artifact workflows |
Related Reading
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Discuss your AI Agent Engineering path
Submit system context, constraints, and delivery pressure. A Principal Engineer reviews every submission and recommends the right next step.
1. Context
We review the system, constraints, and where risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory, sprint, or pause.
3. Next Step
If there is a fit, we define the shortest useful engagement.
No SDRs. A Principal Engineer reviews every submission.