Agents that do the work — and know their limits.
We build production AI agents that plan, use your tools, and take action — voice agents on the phone, coding agents in your repos, and task agents across your workflows. Every one is grounded in your data, evaluated on its steps, and gated by a human where it counts.
What separates an agent from a chatbot.
A chatbot answers. An agent decides, acts, and takes responsibility for a whole task — which is exactly why it has to be engineered, not just prompted.
Planning & reasoning
The agent breaks a goal into steps, decides what to do next, and adapts when a step fails — not a fixed script.
Tool use & actions
Agents call your systems through typed tools — APIs, databases, and MCP servers — to actually do things, not just describe them.
Memory & context
Working memory plus long-term recall keeps an agent coherent across a long task and grounded in your data.
Orchestration & multi-agent
One planner, or a team of specialized agents with clear handoffs — as complex as the job needs, and no more.
Grounding & retrieval
Every decision is anchored to your data and policy through retrieval, so the agent acts on facts, not guesses.
Reliability & recovery
Verifiers, retries, timeouts, and fallbacks catch failure and recover — the line between a demo and production.
Agents that pick up the phone.
Real-time voice agents that talk like a person, act on your systems mid-call, and hand off cleanly — replacing hold music and phone trees with a conversation.
Inbound support
Answers calls, resolves tier-1 issues from your knowledge base and live systems, and transfers to a person with full context.
Appointment scheduling
Books, reschedules, and confirms — checking real availability and updating your calendar or EHR live on the call.
Outbound & reminders
Proactive calls for renewals, payments due, and follow-ups — natural, compliant, and fully logged.
Collections & billing
Respectful payment reminders and self-serve resolution, with escalation to a human for anything sensitive.
Surveys & feedback
Conversational surveys that adapt to each answer and capture structured results — far better response than a phone tree.
IVR replacement
Retire the menu maze — callers say what they need in plain language and get there in a single step.
Voice is unforgiving: responses have to come back in under a second, the agent has to handle interruptions and cross-talk, cope with accents and background noise, honor recording and consent rules, and hand off to a human the moment a call turns sensitive. We engineer for all of it — latency budgets, barge-in, and graceful escalation built in from the start.
Agents that ship code — behind a review.
Repo-aware agents that read your codebase, make changes, run the tests, and open a pull request for your engineers to approve. Developer leverage, not developer replacement.
Legacy migration
Move code across languages and frameworks — the agent reads the repo, ports module by module, and keeps the tests green.
Test generation
Generate unit and integration tests to close coverage gaps, run them, and keep only the ones that pass.
Code review
A review agent flags bugs, security issues, and style problems before a human reviewer opens the pull request.
Dependency upgrades
Bumps versions, fixes the breakages, and proves it with a passing build — routine toil, automated.
Bug triage & fixes
Reproduces the issue, localizes the cause, drafts a fix, and opens a PR for a human to approve.
Docs & onboarding
Keeps READMEs, API docs, and runbooks in sync with the code as it changes.
Coding agents run in sandboxed environments with no access to production or secrets, every change is proven by a passing build in CI, and nothing merges without a human approving the pull request. The agent does the toil; your team keeps the keys and the final say.
An agent takes actions. Govern them.
The difference between a chatbot and an agent is consequence — an agent can send, spend, change, and delete. Governance is how we make that safe: permission, verification, audit, and a human on the decisions that matter.
Least-privilege access
Each agent gets only the tools and data its job requires — scoped credentials, not the keys to everything.
Action approval gates
High-stakes actions pause for a human to approve — the agent proposes, a person disposes.
Trajectory evaluation
We evaluate the steps, not just the final answer — did the agent use the right tools, safely, to get there?
Guardrails & policy
Input and output guardrails, policy checks, and prompt-injection defense keep the agent inside the lines.
Audit & tracing
Every decision, tool call, and input is traced and logged — a full, reviewable record of what the agent did and why.
Limits & kill switch
Rate limits, spend caps, timeouts, and a kill switch — a bounded blast radius when something goes wrong.
A wrong answer from a chatbot is an inconvenience. A wrong action from an agent is an incident. So we scope every agent to least privilege, gate its consequential actions behind a human, evaluate the path it takes and not just the result, and log all of it for audit — because an agent you cannot inspect is an agent you cannot trust in production.
Anatomy of an enterprise AI agent.
An agent is not a single prompt. It is a system: a reasoning core that plans and decides, memory that keeps it coherent, tools that let it act on your systems, a model layer underneath, and interfaces to people and other agents — all wrapped in policy, guardrails, and audit. This is the blueprint we build to.
Every agent workflow, mapped.
The same architecture runs many different flows. Here are the patterns we design and operate — from a single agent thinking through a task to a governed team of agents executing a whole process.
One agent works a goal end to end: it observes the state, plans the next step, chooses a tool, acts, checks the result, and repeats — escalating to a person when it hits its limits.
Before it acts, the agent pulls the facts: it retrieves from your knowledge and policy, grounds its reasoning in that context, then acts and cites what it used — so decisions rest on your data, not guesses.
For complex work, an orchestrator plans and delegates to specialized agents that run in parallel, share state over a message bus, and hand results back for aggregation — a team of digital teammates, each doing what it is best at.
Consequential actions do not just happen. The agent proposes, a risk check routes it — low-risk actions execute automatically, high-risk actions pause for a person to approve, edit, or reject — and every path is logged.
Agents are managed, not shipped-and-forgotten. We design, build, evaluate on both the path and the outcome, deploy, monitor in production, and optimize — a closed loop that keeps agents aligned to results.
How we help you agentify the enterprise.
From first strategy to always-on operation, these are the services we bring — end to end, or wherever you need us in the journey.
Agentic AI consulting
We assess where autonomous agents create real leverage, evaluate agent platforms and frameworks against your stack, and produce an automation and human-in-the-loop strategy with a sequenced target architecture.
Custom agent development
We build domain-specific, context-aware agents for your tasks — sales co-pilots, research assistants, operations agents — wired to your apps, APIs, and workflows from day one.
Multi-agent systems
We design orchestrated teams of agents with clear roles, communication protocols, and control logic to execute complex, cross-function workflows resiliently and in parallel.
Ready-to-deploy agents
Pre-built, enterprise-grade agents for common jobs — modular, extensible, and integration-ready — so you start from a working system, not a blank page.
Agent training & optimization
We tune agents against your business logic, feedback loops, and outcome data — reducing errors, cutting latency, and aligning behavior to the KPIs that matter.
Agent integration
We embed agents into your live systems — CRM, ERP, data platforms, and cloud — so they can act, trigger, and close loops without disrupting existing operations.
Monitoring & support
We run agents with full observability, traceability, and root-cause diagnostics — plus lifecycle management that keeps them healthy long after launch.
Responsible agent governance
We embed least-privilege access, approval gates, bias and safety controls, immutable audit trails, and human oversight across the entire agent lifecycle.
Agents at work across your business.
A library of domain-trained, business-aware agents — built to execute at scale, cut decision latency, and close loops inside your live processes. A sample of what we build, by function.
Account profitability agent
Predicts account-level profitability for loans and recommends segmentation and pricing strategies.
Fair-lending compliance agent
Analyzes lending data for peer benchmarking and disparity checks to support compliance reviews.
Relationship-manager co-pilot
Summarizes client history, researches market moves, and drafts briefs and follow-ups.
Exception triage agent
Predicts transaction failures from operational logs and recommends resolutions before they escalate.
Category performance advisor
Optimizes pricing, promotion, and assortment with real-time insight and automated execution.
Product recommendation agent
Recommends products and cross-sell using behavior signals and vector search.
Marketing content agent
Generates campaign copy and creative at scale, aligned to brand voice and channel.
Social sentiment agent
Tracks social conversation to surface trends and product feedback in real time.
Prior-authorization agent
Assembles clinical evidence, checks policy, and drafts prior-auth submissions for human review.
Clinical-trial summarization
Turns lengthy trial documents into concise summaries for internal teams and filings.
Label extraction agent
Extracts product attributes from labels to support regulatory reporting and compliance.
R&D research agent
Runs semantic search across research and design-history files to accelerate discovery.
Demand-supply mesh
Forecasts demand, optimizes procurement, and recommends profitable promotions autonomously.
Logistics optimization agent
Plans network moves and simulates scenarios to cut handling and transportation cost.
Procurement agent
Automates sourcing steps for savings, compliance, and cycle-time reduction.
AIOps agent
Detects and resolves data and pipeline issues to speed operations and reduce downtime.
What a production agent actually looks like.
The conceptual anatomy above is the idea. This is the deployment — the planes we stand up inside your cloud, and the components that make an agent survivable in production. Names differ per stack; the shape rarely does.
One step, end to end — with a budget.
Every agent step is the same sequence, and every stage owns a latency and failure contract. Publishing these numbers internally is what turns "the agent feels slow" into an engineering conversation.
Four kinds of memory, four different stores.
"Give the agent memory" is not one decision. These are separate concerns with different lifetimes, stores, and failure modes — and conflating them is why agents either forget the last turn or drag irrelevant history into every call.
The tool layer is where agents actually fail.
In our experience more agent defects trace to tool design than to the model. A tool is an API contract written for a reader that cannot ask a follow-up question — so it gets the same rigor as a public interface.
Typed schemas, versioned
JSON Schema for every argument and return, semantic versioning, and a deprecation path. A tool whose shape changes silently breaks agents that were passing yesterday.
On-behalf-of, not god mode
Agents act with the calling user's scope through token exchange wherever possible. A service account with broad rights is the most common privilege-escalation path we find in agent reviews.
Errors that say what to do
"Invalid request" teaches a model nothing. "start_date must precede end_date; received 2026-03-01 and 2026-02-01" gets a correct retry on the next step instead of three wasted ones.
Read, write, and irreversible
Every tool is classified. Reads run freely, writes run within bounds, and irreversible actions — payments, external messages, deletions, production config — require an approval that is recorded.
Four shapes. Pick the least agentic one that works.
Agency is a cost paid in reliability, so it should buy something. We start from the most constrained topology that solves the problem and add freedom only where the path genuinely cannot be known in advance.
Single agent + tools
One model in a loop with a bounded tool set.
Best for tasks under roughly ten steps with a homogeneous skill set. Highest reliability, easiest to evaluate and debug. Start here and only move if the task genuinely does not fit.
Supervisor + specialists
A planner decomposes the task and delegates to narrow worker agents, then assembles their results.
Use when subtasks need genuinely different tools or context, or can run in parallel. Each worker stays narrow, which keeps step reliability high — but the supervisor becomes the failure point, so its planning gets its own eval set.
Deterministic pipeline
A fixed DAG where model calls sit at specific nodes and control flow is ordinary code.
The most reliable shape available, and the right answer far more often than teams expect. If the path is known in advance, do not make the model rediscover it on every run.
Generator + critic
One agent produces, a second verifies against explicit criteria and can send it back.
Use where verification is cheaper than generation and quality matters more than latency — drafting, code changes, anything customer-visible. Costs roughly double; often worth it.
Guardrails at three points, not one.
Safety is not a filter bolted to the output. It is enforced before the model sees the request, inside the loop where actions are chosen, and after generation before anything lands.
CI/CD for something non-deterministic.
An agent release is not a prompt edit. The prompt, tool schemas, model version, retrieval configuration, and policy are one versioned artifact — because changing any of them changes behaviour, and you need to know which one did.
Bring us a hypothesis. Leave with a system.
Tell us what's eating your team's time. We'll give you an honest read on whether AI is the right tool — and if it is, a scoped v1 with a timeline and cost.