AI Frameworks & Agent Orchestration
Most AI agents break in production.
Here is how to build ones that do not.
We design, build, and rescue AI agent systems for mid-market firms using the frameworks that actually fit your stack - LangChain, LangGraph, CrewAI, AutoGen, and custom orchestration layers built on top of your existing platforms.
Book a strategy call$250M+
Pipeline generated
42%
Average pipeline growth
18.3%
Average budget saved
Results from actual client engagements.
Proof-of-concept agents work. Production agents are a different problem entirely.
Most mid-market teams reach the same wall. A developer or vendor builds an impressive demo - an agent that summarizes calls, routes leads, or drafts proposals. Leadership approves a broader rollout. Then the agent hits real data: inconsistent CRM fields, ambiguous prompts, tools that time out, and edge cases nobody anticipated. The system hallucinates, loops, or silently returns wrong answers. Nobody has a clear picture of what the agent actually did or why. The project stalls, trust erodes, and the technology gets blamed instead of the architecture.
The underlying issue is that frameworks like LangChain, LangGraph, and CrewAI give you powerful primitives - chains, graphs, memory, tool-calling, multi-agent coordination - but they do not give you a production system. You still need deterministic guardrails, structured observability, graceful failure handling, and a data layer that is clean enough for an agent to reason over reliably. That engineering work is where most implementations fall apart, and it is exactly where we focus.
The AI Frameworks & Agent Orchestration platforms we specialize in
Pick your platform. We'll make it deliver.
AI SDK
We architect and ship AI agents using Vercel AI SDK - wiring streaming completions, tool-calling, and multi-step reasoning into the CRM, ERP, and data systems your revenue team already runs on.
Explore AI SDKCrewAI
We design, build, and stabilize CrewAI crews for mid-market revenue and operations teams - defining roles, tasks, and tool integrations so your agents do real work without constant babysitting.
Explore CrewAIGoogle ADK
We design and build multi-agent systems on Google's Agent Development Kit - wiring Gemini models, tool calling, session state, and evaluation into workflows your operations team can actually run and maintain.
Explore Google ADKLangChain
We architect, build, and stabilize LangChain agent pipelines - chains, RAG retrieval, tool-calling agents, and memory - so your AI workflows run reliably in production, not just in demos.
Explore LangChainLangGraph
We design and build stateful, multi-agent LangGraph workflows that actually hold up in production - handling branching logic, memory, human-in-the-loop checkpoints, and the edge cases your first prototype never anticipated.
Explore LangGraphLlamaIndex
We build and operationalize LlamaIndex retrieval pipelines, agent workflows, and data connectors on your actual corpus - contracts, CRM exports, product docs, financial records - so the answers your team gets are accurate and traceable.
Explore LlamaIndexMicrosoft AutoGen
We design, build, and operationalize Microsoft AutoGen agent pipelines for mid-market companies - connecting AssistantAgent, UserProxyAgent, and GroupChat workflows to the real systems your revenue and ops teams depend on.
Explore Microsoft AutoGenSemantic Kernel
We design and build Semantic Kernel agents that go beyond demos - wiring planners, plugins, memory stores, and process automation into the CRMs, ERPs, and data sources your team actually uses.
Explore Semantic KernelWhy mid-market firms bring us in for AI agent work
Architecture before any code is written
We map your actual workflows, data sources, and failure tolerance before recommending a framework or writing a single prompt. LangGraph is the right call when you need stateful, branching logic. CrewAI fits role-based multi-agent tasks. A simple LangChain pipeline often outperforms an over-engineered multi-agent system. We make that call based on your operations, not on what is trending.
Clean data layer as a prerequisite
An agent is only as reliable as the data it reasons over. We audit your CRM records, document stores, and structured outputs before wiring any agent to them. Dirty data does not just produce bad answers - it produces confidently wrong answers that users act on. We fix the source before connecting the agent, which is the step most implementations skip.
Observability and tracing built in from day one
We instrument every agent with LangSmith, Langfuse, or equivalent tracing so you can see exactly what prompt was sent, what tool was called, what was returned, and where latency or errors occurred. Without this, debugging a misbehaving agent in production is guesswork. With it, you have an audit trail that also feeds continuous improvement.
Deterministic guardrails over pure LLM reasoning
We design agents with explicit fallback paths, output validation schemas, and human-in-the-loop checkpoints where the cost of a wrong answer is high. Fully autonomous agents are appropriate for narrow, low-stakes tasks. For anything touching revenue, compliance, or customer data, we build in structured checkpoints that catch errors before they propagate.
Integration with platforms your team already uses
We connect agent outputs to HubSpot, Salesforce, NetSuite, Slack, and other systems your team lives in - not a new dashboard nobody checks. That means agents that update deal stages, create tasks, send notifications, or populate reports inside the tools your operators already trust, without requiring workflow changes to capture the value.
Rescue and optimization of existing agent builds
If a previous build is unreliable, too slow, or too expensive to run at scale, we diagnose the architecture and fix it. Common problems include unbounded agent loops, missing memory management, over-reliance on large context windows instead of retrieval, and prompt designs that degrade with real-world input variation. We have seen most of the failure modes and know how to resolve them.
What AI agent orchestration actually involves for a mid-market operator
Agent orchestration is the layer that decides what an AI system does next: which tool to call, which sub-agent to invoke, whether to ask for clarification or proceed, and what to do when a step fails. Frameworks like LangGraph model this as a directed graph where each node is a function and edges represent conditional transitions. CrewAI models it as a team of specialized agents with defined roles and a process that coordinates their outputs. LangChain provides the lower-level building blocks - tool wrappers, memory interfaces, prompt templates, output parsers - that both higher-level frameworks and custom builds rely on.
For a mid-market firm, the practical question is not which framework is most sophisticated. It is which one your team can maintain, which one fits the structure of the task you are automating, and which one connects cleanly to the systems you already run. A sales agent that qualifies inbound leads needs to read CRM data, apply business logic, write back a score, and possibly trigger a sequence. That is a well-defined, linear task that does not require a multi-agent graph. An agent that manages a complex RFP response - pulling from multiple document sources, coordinating a legal review step, formatting output to a template - has genuine branching and role-based logic that benefits from a more structured orchestration approach.
Where implementations break and how to avoid the common failures
The most frequent failure mode is building an agent that works on clean, representative test data and then deploying it against production data that is inconsistent, incomplete, or formatted differently than expected. LLMs are tolerant of variation in ways that feel like a feature until the agent confidently processes a malformed record and writes a wrong value back to your CRM. Structured output validation - using Pydantic models, JSON schema enforcement, or framework-native output parsers - catches this class of error before it propagates. It is not optional for production systems.
The second common failure is unbounded agent loops. When an agent is given a goal and a set of tools, it can get stuck retrying a failing tool call, oscillating between two states, or generating increasingly long context windows that eventually exceed model limits and error out. LangGraph addresses this with explicit cycle detection and maximum iteration controls. Any production agent needs defined exit conditions, not just a success path.
Memory management is the third area where mid-market implementations underperform. Passing an entire conversation history or document set into every LLM call is expensive and eventually hits context limits. The right approach depends on the task: short-term buffer memory for conversational agents, vector store retrieval for document-heavy tasks, and structured state objects for multi-step workflows where specific values need to persist across steps. Getting this right is what separates an agent that runs reliably at scale from one that works in a demo and degrades in production.
AI Frameworks & Agent Orchestration questions, answered
Which AI agent framework should we use?
It depends on the task structure. LangGraph is well-suited for workflows with branching logic and state that needs to persist across steps. CrewAI works well when you want distinct agent roles collaborating on a task. LangChain is a reasonable default for linear retrieval-augmented generation and tool-calling pipelines. AutoGen fits research-style multi-agent conversations. We assess your specific use case, your team's ability to maintain the code, and your infrastructure before recommending anything.
Can we build AI agents without a dedicated engineering team?
For simple, narrow tasks - yes, with the right scaffolding and low-code tooling layered on top. For anything that touches multiple systems, requires reliable output formatting, or runs autonomously on production data, you need engineering involvement at least during the build and testing phases. We can build and hand off, build and maintain, or upskill your internal team depending on what makes sense for your situation.
How do we know if an agent is actually working correctly?
You need tracing and evaluation in place. That means logging every LLM call with its inputs, outputs, and latency, running structured evals against known test cases, and monitoring for output drift over time as your data or prompts change. Without this infrastructure, you are flying blind. We treat observability as a first-class deliverable, not an afterthought added after something breaks.
What is retrieval-augmented generation and when does it matter?
RAG is the pattern of pulling relevant documents or records from a vector store or search index and including them in the prompt context before the LLM generates a response. It matters any time you want an agent to answer questions about your own data - contracts, product documentation, CRM notes, support tickets - rather than relying on general training knowledge. Getting the chunking, embedding model, and retrieval logic right is where most RAG implementations underperform.
How much does it cost to run AI agents at scale?
Token costs vary significantly by model choice, context window size, and call frequency. An agent that passes large documents to GPT-4o on every run will cost materially more than one using a smaller model with targeted retrieval. We design for cost efficiency by right-sizing models to tasks, caching where appropriate, and avoiding unnecessary LLM calls. We also help you forecast operational costs before you commit to a production architecture.
How long does it take to go from idea to a working production agent?
A focused, single-purpose agent with clean data inputs and a defined output format can reach a reliable production state in a few weeks. Multi-agent systems with complex routing, multiple tool integrations, and stateful memory take longer - typically several weeks to a few months depending on data readiness and integration complexity. The biggest variable is almost always data quality, not the framework itself.
Do you build on proprietary platforms or open-source frameworks?
Both, depending on what fits. Open-source frameworks like LangChain and LangGraph give you full control and avoid vendor lock-in, which matters for mid-market firms that want to own their stack. Proprietary platforms like certain no-code agent builders can accelerate simple use cases but introduce dependency and cost risk at scale. We are vendor-agnostic and will tell you honestly when a proprietary tool is the right call and when it is not.
Not sure which AI Frameworks & Agent Orchestration platform fits?
We're vendor-agnostic. Tell us your goals and we'll recommend the right stack - then build it.
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