01
Planner
Thinks before it acts.
An LLM reasoning loop that decomposes the goal into steps — and can backtrack when a step fails.
- Goal decomposition
- Reflection on failures
- Bounded autonomy
Agentic AI development
Agents that actually do the work.
Production-grade AI agents wired into Slack, Linear, your CRM and your internal APIs. Multi-step reasoning, human-in-the-loop, and evals built in from day one — without the demo-day theatrics.
Production-grade evals
Human-in-the-loop by default
PII-safe execution
Featured at AI summits
SupportAgent · trace
run #4821User
Refund the last order from sarah@acme.io and send a kind follow-up.
thought
Need order id → lookup customer → issue refund → draft email.
tool · crm.getCustomer
→ id=acme_3294
tool · stripe.refund
→ ch_1NaB · ₹4,890
tool · gmail.draft
→ draft prepared (Sarah)
hitl · awaiting approval
4 tools · 1.4s · $0.0123✓ eval passed
Completion
99.5% ↑
Saved / mo
12k hrs
40+
Agents in production
99.5%
Task completion rate
12k+
Hours saved / month
50+
Tools integrated
Anatomy of an agent
Four parts. All four, on purpose.
An agent isn't a prompt — it's a system. We build each part deliberately, and we tell you honestly if you don't need an agent at all.
02
Tools
Reaches into your stack.
Typed tool calls into your APIs, SaaS apps and internal services — sandboxed, rate-limited, audited.
- Typed JSON schemas
- Rate-limit + retries
- Per-tool audit trail
03
Memory
Remembers what matters.
Short-term context plus long-term vector store, scoped per tenant and per user with redaction policies.
- Per-tenant isolation
- Vector + keyword recall
- Redaction & retention
04
Evals
Proves it actually works.
Offline golden sets and live production evals, so changes ship behind measurable gates — not vibes.
- Golden eval sets
- Live regression dashboards
- Cost / latency budgets
The agent stack
Frontier models, boring infrastructure — exactly where each belongs.
Every tool below is on a live agent this quarter — not an aspirational architecture diagram.
Anthropic Claude
Reasoning
OpenAI
Reasoning
LangChain
Orchestration
Vercel AI SDK
Runtime
Python
Backend
Supabase
Vectors + state
Slack
Surface
Linear
Workflow
Why now
The cheapest houryour business will ever buy.
Models are good enough. Tool-use is reliable. Eval and observability tooling has caught up. The bottleneck isn't the AI any more — it's the engineering discipline around it. That's the part most demos quietly skip and most production deployments fail on.
A well-built agent compounds: it learns from every run, gets cheaper as models improve, and frees senior people to work on what only humans can do. A badly-built one wastes budget, leaks data and erodes trust.
62%
avg. workload that an agent can handle
4×
throughput vs a junior teammate
0
drift if evals are wired right
Agent types
Six workloads where agents already pay back.
We'll only recommend an agent where the workflow is repeatable, the tools are reachable, and there's a real metric to move.
01Support
Customer support agents
Resolve tier-1 tickets across email, chat and WhatsApp — escalate cleanly when they should.
- Zendesk / Intercom / Freshdesk
- Sentiment-aware escalation
- Knowledge-grounded replies
02Sales
Outbound & SDR agents
Research accounts, draft personalised outreach and book meetings — under your sales playbook.
- LinkedIn + email sequences
- ICP scoring
- Calendar handover
03Research
Research assistants
Brief generation, market scans, competitor monitoring — with cited sources you can audit.
- Cited briefs (Markdown)
- Scheduled monitoring
- Slack / Notion publishing
04Ops
Internal ops agents
Triage tickets, classify documents, update CRM records — quietly running on your back-office work.
- Doc classification
- CRM enrichment
- Approval workflows
05Engineering
Code & PR agents
Open scoped pull requests, run on-call playbooks, monitor flaky tests — alongside your engineers.
- PRs behind feature flags
- On-call assist
- Test-flake triage
06Knowledge
Internal knowledge agents
Surface the right doc, ticket or decision — across Notion, Linear, Drive and your data warehouse.
- Notion / Drive / Linear
- Role-based access
- Citation-first answers
Agents in production
Shipped, evaluated, still compounding.
B2B SaaS · Support
Tier-1 support agent cleared 62% of inbound without escalation.
−62%human handling time
FinTech · Sales
Outbound agent booked meetings 4× faster than a junior SDR.
4×meetings booked / week
Marketplace · Ops
Ops agent processed 18k merchant docs / month, zero manual review.
99.4%classification accuracy
Our approach
Workflow to productionin six fixed stages.
Evals before vibes. Shadow-mode before automation. Cost dashboards before the first invoice — not after.
Avg. build: 6–10 weeks
- 01
Workflow discovery
We sit with the team, watch the work, and pick the loop where an agent will actually compound.
Deliverables: Workflow map · Use-case scoring
Week 1 - 02
Tool & data design
Tool surfaces, schemas and the memory model — what the agent can touch and what it absolutely cannot.
Deliverables: Tool schemas · Memory plan
Week 2 - 03
Eval suite first
Before the agent runs in anger, we build the golden set. Changes ship behind measurable gates from day one.
Deliverables: Golden set · Eval harness
Week 2 - 04
Agent build
Planner + tools + memory wired with bounded autonomy. Human-in-the-loop where stakes are high.
Deliverables: Releasable agent · HITL UI
Weeks 3–6 - 05
Shadow mode
The agent runs alongside humans on real workload. We measure agreement, cost and latency before flipping the switch.
Deliverables: Shadow-mode report
Week 7 - 06
Production & retro
Staged rollout behind flags. Live evals, cost dashboards and a quarterly retro on what the agent should learn next.
Deliverables: Live dashboards · Retro
Ongoing
Standard package
Evals, audit trails, cost dashboards
What's included
Every agent shipswith the system, not just the prompt.
Specifications you own, evals you can audit, and cost dashboards that tell the truth at 3am.
- 01Custom agent built against a written specification you own
- 02Typed tool schemas + auditable run logs for every step
- 03Golden eval set + live production regression dashboards
- 04Human-in-the-loop UI for any high-stakes action
- 05Cost, latency and token-budget dashboards in Looker Studio
- 06PII redaction + per-tenant memory isolation by default
- 07Slack / Linear / email notification routing
- 0830-day post-launch tuning window — prompt and eval iteration
FAQ
Honest answersbefore you ask.
Can't find what you're looking for? Send a brief — we reply within a business day.
- 01
How is an AI agent different from a chatbot?
A chatbot answers. An agent does. It plans a goal into steps, calls real tools in your stack (CRM, ticketing, internal APIs), reasons over the result, and either finishes the task or hands off to a human. The work, not just the conversation.
- 02
What does "production-grade" mean for you?
Evaluated. Bounded. Observed. Every agent ships behind a golden eval set, sandboxed tool execution, audit trails on every step, and dashboards for cost, latency and task-completion. Changes pass evals before they roll out.
- 03
How do you keep our data safe?
Per-tenant memory isolation, configurable PII redaction, scoped API keys for every tool call, and the option to host on your cloud (AWS / GCP / Azure). We sign DPAs on request and align to your data-residency rules.
- 04
How long does an agent take to ship?
A focused single-workflow agent ships in 4–6 weeks including evals and shadow-mode. A multi-tool agent across several systems usually takes 8–12 weeks. We give a realistic estimate after the workflow-discovery call.
Let's scope
Got a workflow that should already be agentic?
Book a workflow-discovery call. We'll map the loop, score the use-case, and tell you honestly whether an agent is the right answer — no decks, no pressure.
Production-grade evals
Human-in-the-loop by default
PII-safe execution
