Every engagement starts by asking where intelligence genuinely helps. LLM pipelines, agentic workflows, and AI features that replace real manual overhead.
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See All Projects →Agents are not magic. They are software with judgment, and judgment is the part that breaks most often. We build agentic workflows that hold up in production — honest about what AI can and cannot reliably do.
The first question is almost never which framework. It is which framing fits your problem.
Steps are mostly known. The LLM is the brain at specific decision points — decide a path, classify an input, write a response — but the surrounding flow is deterministic code you control.
Steps are not knowable in advance. The system needs to plan based on what it discovers. Research tasks, complex multi-system operations, anything where the path is genuinely emergent.
In practice, most things people call "agents" are actually structured workflows with LLM decision points. That is a feature, not a bug. We will tell you which one you actually need.
Cutting through the marketing — this is what an agent actually is, and where bad ones fail.
Not a prompt, a goal. "Qualify this lead." "Find all references to this clause in our contracts." "Investigate why this customer is unhappy."
Decides which steps to take, in what order, with what information. This is the part that requires genuine AI judgment.
APIs, database queries, search, your existing systems. The agent is not just talking — it is doing things in the real world.
Within a task and sometimes across tasks. Memory is what lets it not repeat itself or contradict its own earlier decisions.
Either it completes the task and reports, or it recognises it cannot and hands off cleanly. Most failures happen here — silently failing rather than escalating.
Each type has a different risk profile, failure mode, and design requirement.
Take a question, gather information from your data and external sources, synthesise an answer with citations. Used for competitive intelligence, customer research, market analysis, due diligence. The simplest place to start — a weak answer is recoverable.
Run a business process end-to-end with judgment at the decision points. Lead qualification, ticket triage and routing, onboarding flows, refund processing. Significant lift over manual processing — big trust requirement, careful design needed.
Agents inside your product helping users get things done. Search, summarise, take actions on behalf of the user, automate multi-step flows. Related to our AI copilot and chatbot work.
Pure agentic systems sound impressive in demos and break frequently in production. Our default approach is boring on purpose.
The overall flow is regular code. The LLM is called only at specific decision points where its judgment is genuinely needed. Debuggable, predictable, cheaper to run.
We give the agent the smallest set of tools that covers the job. Smaller action space means fewer ways to go wrong.
What if the API call fails? What if the LLM produces invalid output? What if the agent loops? Fallbacks, retries, and escalation paths are designed before we ship.
Every reasoning step, every tool call, every input and output is logged and traceable. When something goes wrong — and it will — we can see exactly what happened.
For high-stakes decisions, the agent proposes and a human approves. For lower-stakes, the agent acts and humans review batches after the fact. We design the loop based on the stakes.
Three areas that separate production agents from broken demos.
Short-term memory lets the agent reason coherently across steps. Long-term memory lets it apply what worked before. We use vector stores for semantic memory, structured databases for facts, and clear retention policies so memory does not become a data privacy problem.
The functions the agent can call — APIs, database queries, search, file operations, calls to your existing services. We define each tool tightly (clear inputs, clear outputs, validated arguments) so the agent cannot accidentally make a mess.
Agents are harder to evaluate than simple LLM calls because there are many steps and many possible paths. We build harnesses that test end-to-end (did the agent achieve the goal?) and step-by-step (was each decision reasonable?). Without this, you have no idea if a change made things better or worse.
Chosen based on project requirements — not defaulted to the most popular option.
Real agent builds, real outcomes — the problem, the architecture, and what shipped.
Built a voice agent that proactively calls customers, guides them through application steps in natural conversation, and writes back to the same loan state the web app reads — so a customer helped by the agent picks up exactly where they left off online.
Built a modular KYC engine for SBNRI where every regulatory step is independent and composable across products. A customer verified for mutual funds carries that KYC into FDs, banking, and tax filing — resuming from the exact step they left, on web, iOS, Android, or WhatsApp.
Yes, with careful design. Pure autonomous agents are still unreliable for most production use cases. Structured workflows with LLM decision points, narrow action spaces, observability, and human-in-the-loop are production-ready and increasingly common. We design for what works, not what makes a great demo video.
LangGraph when the agent has explicit state and control flow that matters — which is most non-trivial agents. LangChain for simpler workflows or when the ecosystem (integrations, retrievers, document loaders) is the actual value. Sometimes neither, when the project does not need a framework. We pick based on the project.
Constrain the action space. Validate every tool call before execution. Add step limits and budget limits (cost, time, number of LLM calls). Require human approval for high-stakes actions. Log everything and run evaluation continuously. Most "agent went wild" stories trace back to systems missing one or more of these.
Depends on usage volume, model choice, and how well the system is engineered. A well-designed agent that uses cheap models for easy steps and expensive models only when needed can be very economical. A poorly designed agent that calls GPT-4o on every step can get expensive fast. We model the per-run and per-month cost as part of scoping.
Not in the way the hype suggests. They will absorb specific routine tasks that used to take up large parts of certain roles. The people in those roles tend to get pushed up the value chain, doing the harder work the agent escalates to them. We have honest conversations about this in scoping.