HighPixel /Services /Layer 01 · Intelligence /Agentic AI Systems
Layer 01 · Intelligence

Agentic AI systems
that do the work.

Autonomous agents that plan, call your tools, remember context, and improve from feedback — wired into the systems your business already runs on. Not a chatbot wrapper. An operator that ships work end-to-end.

Engagement
6–14 weeks
discovery → pilot → prod
Surface
Slack · Web · API · Voice
deploy where users live
Stack
Claude · LangGraph · pgvector
model-agnostic core
LIVE · agent.runtime
01 · WHY AGENTS

Where agents earn their seat at the table

Three problems we see across every engagement — and the agentic shape of the answer.

01

Static workflows can't keep up

Real work isn't a flowchart. Edge cases dominate. Agents reason at runtime instead of routing every decision through a human.

02

Models alone aren't enough

An LLM in a chat box is a demo. Agents bring tools, memory, eval, guardrails — the production scaffolding that turns intelligence into shipped outcomes.

03

Trust is engineered, not promised

Every action is traced, every output is evaluated, every escalation is policy-defined. You see what the agent did and why.

02 · WHO IT'S FOR

What we ship, organised by the person who feels the problem

persona

CTO / VP Engineering

Pain
We've prototyped agents in isolation but can't get past 'it works in the demo.'
What we ship
A reference architecture, deployed in your VPC, with eval/audit/observability your team can extend.
persona

Risk & Compliance

Pain
Auditors want to know what the AI did, why, and whether a human signed off.
What we ship
Append-only trace ledger, policy-as-code guardrails, and a human-in-the-loop console that maps to your control framework.
persona

VP Product

Pain
Customers want self-serve outcomes. Our team wants leverage, not chatbots.
What we ship
An agent surface (Slack, web, API) that completes real journeys — with eval baselines, A/B framework, and rollback baked in.
persona

Operations Lead

Pain
Half my team is doing the same 12-step process across tools, every day.
What we ship
An ops agent that executes the playbook, escalates the genuine exceptions, and writes back to your systems of record.
03 · WHAT WE BUILD

Six capabilities, composed into one agent.

No one piece is interesting on its own. Together they're the difference between a demo and a production system.

Orchestration

Stateful plan-act-reflect loops that survive long-running work.

  • LangGraph / custom DAGs
  • Resumable from any checkpoint
  • Multi-agent coordination patterns

Tool Routing

Capability registry where agents pick the right tool for the job — and only the right tool.

  • Typed tool schemas with validation
  • Per-tool permissions & rate limits
  • MCP & OpenAPI native

Memory & Retrieval

Episodic and semantic memory the agent can actually rely on across sessions.

  • Vector + structured stores
  • Per-tenant isolation
  • Forgetting policy by default

Eval Harness

Continuous quality measurement — not a one-time benchmark.

  • Golden sets + LLM-as-judge
  • CI gates on every change
  • Drift alerts in production

Guardrails

Policy as code. Refuse, redact, or escalate — same rule everywhere.

  • Input + output filters
  • PII redaction & masking
  • Hard human-in-loop boundaries

Trace & Audit

An append-only record of every plan, tool call, and decision.

  • Step-level diff & replay
  • Signed action ledger
  • SOC 2 / ISO 27001 ready
04 · REFERENCE ARCHITECTURE

The shape of a production agent

Four lanes — ingress, orchestration, capability, audit. Every engagement starts here and gets shaped to your stack.

Lane 01 · Ingress

Chat, web, API, voice, or a scheduled trigger. The agent meets the user where they already are.

Lane 02 · Orchestration

Plan → Act → Reflect loop. Resumable, observable, model-agnostic. The brain of the agent.

Lane 03 · Capability

Tools, memory, model gateway. Versioned, permissioned, and instrumented down to the call site.

Lane 04 · Audit & Eval

Append-only trace ledger and continuous eval harness. Trust is something the system can prove.

05 · ENGAGEMENT

From discovery to production in roughly 12 weeks

Four phases, fixed deliverables per phase, and a real exit ramp at the end of each — so you can stop, ship, or extend.

01
Wk 1–2

Discovery & frame

We map one high-leverage workflow end-to-end — the people, the systems, the failure modes — and write a one-pager that defines what 'good' looks like.

Deliverables
▸ Process map
▸ Eval rubric v0
▸ Risk register
02
Wk 3–6 · ACTIVE EXAMPLE

Pilot agent

A working agent against a realistic eval set. Tools wired, guardrails on, trace ledger live. Internal users in the loop, not customers yet.

Deliverables
▸ Agent v1 in your VPC
▸ Golden eval set
▸ Internal pilot console
03
Wk 7–10

Hardening

We shake the tree: red-team the prompts, stress the tools, tune the retrieval, instrument what's missing. Pass criteria become CI gates.

Deliverables
▸ Red-team report
▸ CI eval pipeline
▸ Runbook & on-call rotation
04
Wk 11+

Production & handover

Phased rollout with a kill switch. Your team takes the wheel; we stay on retainer for capability expansion and drift response.

Deliverables
▸ Prod deployment
▸ Drift dashboard
▸ Capability backlog
06 · THE STACK

Model-agnostic on purpose

We pick the boring, durable piece by default — and the cutting edge where it actually moves your metric.

Models & Reasoning
Claude (primary)GPT-4oLlama 3.xOpen-weight self-hostBedrockVertex AI
Orchestration
LangGraphMCPInngestTemporalCustom DAG
Memory & Retrieval
Postgres + pgvectorPineconeWeaviateRedisOpenSearch
Eval & Observability
LangfuseBraintrustOpenTelemetryDatadogCustom rubrics
Guardrails & Safety
NeMo GuardrailsCustom policy DSLPII detectionOutput validators
Deploy
Your VPC (AWS · GCP · Azure)KubernetesCloudflare WorkersServerless
07 · OUTCOMES

What changes after the agent ships

Aggregated across the agent engagements we've shipped over the last 18 months.

0%
Median deflection of tier-1 work
Across support, ops, and internal-tooling agents.
0.0x
Faster time-to-action
From request received to system-of-record updated.
0%
Eval pass rate at production cutover
Above the agreed quality bar; the rest go to a human.
<0h
From prod incident to drift fix
Eval harness + trace ledger make root-cause fast.
OF
OUTFITKART · COMMERCE

A merchandising agent that closes the loop on the catalogue.

Read full case →

The agent watches sales velocity, generates copy + image variants, and ships them to the storefront — with a human approving the top 5% only.

2.8×
lift in CTR on agent-managed PDPs
MR
MERIDIAN BANK · OPS

A reconciliation agent for the back office.

Read full case →

Reads SWIFT messages, matches against ledger, drafts the JE, and pages a human only on the genuinely weird ones — replacing a 12-step daily process.

91%
of break tickets auto-resolved
08 · FAQ

Questions we get on the discovery call

Yours, by default. We deploy into your VPC (AWS, GCP, or Azure) with your IAM and your observability stack. We never become a runtime dependency you can't replace.

The orchestration layer is model-agnostic. We default to Claude for reasoning and a smaller open-weight model for routing/cheap calls, then tune per workload after eval. You keep the option to swap.

Three layers: typed tool schemas with strict validation, an eval harness with CI gates on every change, and an append-only trace so every action can be replayed and reasoned about post-hoc.

PII detection and redaction at the prompt boundary, per-tenant memory isolation, and a no-train-on-our-data clause in every model contract. We design to your data residency requirements from day one.

That's the goal. Every engagement ends with a working CI/eval pipeline, a runbook, and a paired-coding handover. Most clients move to a smaller retainer for capability expansion only.

A pilot agent typically runs ₹35–80L over 6–10 weeks depending on tool surface area and compliance scope. Production hardening is scoped separately after the pilot lands.

Continue · Layer 01 · Intelligence
02 · INTELLIGENCE

Decision Intelligence →

ML systems that turn signals into actions — recsys, ranking, forecasting, propensity. Less narrative, more leverage.
03 · INTELLIGENCE

Adaptive UX & Personalization →

Interfaces that respond to who's using them — segment of one, in real time, without dark patterns.
Q2 2026 · two slots open for Agentic AI

Talk to a Agentic AI engineer.

Bring the messy bit. We come back with an architecture sketch and a discovery plan inside two business days — no sales theatre.

response within
48h